This document describes an improved direct multiple shooting approach combined with collocation and parallel computing to handle path constraints in dynamic nonlinear optimization problems. It combines direct multiple shooting with collocation discretization to transform the dynamic optimization problem into a nonlinear programming problem. The approach discretizes the time horizon into finite elements and applies collocation at the nodes. It then uses parallel computing to simulate each time interval independently. Case studies on controlling a Van der Pol oscillator and continuous stirred tank reactor are presented to demonstrate the method.
The issues about maneuvering target track prediction were discussed in this paper. Firstly, using Kalman filter which based on current statistical model describes the state of maneuvering target motion, thereby analyzing time range of the target maneuvering occurred. Then, predict the target trajectory in real time by the improved gray prediction model. Finally, residual test and posterior variance test model accuracy, model accuracy is accurate.
A walk through the intersection between machine learning and mechanistic mode...JuanPabloCarbajal3
Talk at EURECOM, France.
It overviews regression in several of its forms: regularized, constrained, and mixed. It builds the bridge between machine learning and dynamical models.
The document discusses triangular norm (t-norm) based kernel functions and their application to kernel k-means clustering. It introduces common kernel functions and describes how t-norms can be used to create new kernel functions. Several parameterized and non-parameterized t-norm based kernel functions are presented. The document then details experiments applying various kernel functions including t-norm kernels to four datasets, evaluating the results using adjusted rand index scores. The best performing kernels for each dataset are identified, with some t-norm kernels performing comparably or better than traditional kernels.
1. The document discusses the integration of system identification (SYSID) methods with model predictive control (MPC).
2. It describes how SYSID can be used to estimate process models, which are then used for prediction in MPC. The model estimates are also regularly updated using new process data to adapt the MPC predictions over time.
3. However, the document notes that while the components of SYSID and MPC are established individually, fully integrating them in software in a systematic way remains a challenge, particularly for complex multi-variable systems.
This document provides a course calendar and lecture plans for topics related to Bayesian estimation methods. The course calendar lists 12 class dates from September to December covering topics like Bayes estimation, Kalman filters, particle filters, hidden Markov models, supervised learning, and clustering algorithms. One lecture plan provides details on the hidden Markov model, including the introduction, definition of HMMs, and problems of evaluation, decoding, and learning. Another lecture plan covers particle filters, including the sequential importance sampling algorithm, choice of proposal density, and the particle filter algorithm of sampling, weight update, resampling, and state estimation.
The document provides details on a course calendar and lecture plan for hidden Markov models (HMM).
1) The course calendar covers topics like Bayesian estimation, Kalman filters, particle filters, hidden Markov models, supervised learning, and clustering algorithms over 14 weeks.
2) The HMM lecture plan introduces discrete-time HMMs and their applications. It covers the three main problems of HMMs - evaluation, decoding, and learning. Evaluation calculates the probability of an output sequence, decoding finds the most probable hidden state sequence, and learning estimates model parameters from training data.
3) The trellis diagram and forward algorithm are described for solving the evaluation problem, while the Viterbi and forward-backward algorithms are mentioned
When models are defined implicitly as systems of differential equations with no closed form solution, the choice of discretization grid for their approximation represents a trade-off between accuracy of the estimated solution and computational resources. We apply principles of statistical design to a class of sequential probability based models of discretization uncertainty for selecting the optimal discretization grid adaptively. Our proposal is compared to other approaches in the literature.
The issues about maneuvering target track prediction were discussed in this paper. Firstly, using Kalman filter which based on current statistical model describes the state of maneuvering target motion, thereby analyzing time range of the target maneuvering occurred. Then, predict the target trajectory in real time by the improved gray prediction model. Finally, residual test and posterior variance test model accuracy, model accuracy is accurate.
A walk through the intersection between machine learning and mechanistic mode...JuanPabloCarbajal3
Talk at EURECOM, France.
It overviews regression in several of its forms: regularized, constrained, and mixed. It builds the bridge between machine learning and dynamical models.
The document discusses triangular norm (t-norm) based kernel functions and their application to kernel k-means clustering. It introduces common kernel functions and describes how t-norms can be used to create new kernel functions. Several parameterized and non-parameterized t-norm based kernel functions are presented. The document then details experiments applying various kernel functions including t-norm kernels to four datasets, evaluating the results using adjusted rand index scores. The best performing kernels for each dataset are identified, with some t-norm kernels performing comparably or better than traditional kernels.
1. The document discusses the integration of system identification (SYSID) methods with model predictive control (MPC).
2. It describes how SYSID can be used to estimate process models, which are then used for prediction in MPC. The model estimates are also regularly updated using new process data to adapt the MPC predictions over time.
3. However, the document notes that while the components of SYSID and MPC are established individually, fully integrating them in software in a systematic way remains a challenge, particularly for complex multi-variable systems.
This document provides a course calendar and lecture plans for topics related to Bayesian estimation methods. The course calendar lists 12 class dates from September to December covering topics like Bayes estimation, Kalman filters, particle filters, hidden Markov models, supervised learning, and clustering algorithms. One lecture plan provides details on the hidden Markov model, including the introduction, definition of HMMs, and problems of evaluation, decoding, and learning. Another lecture plan covers particle filters, including the sequential importance sampling algorithm, choice of proposal density, and the particle filter algorithm of sampling, weight update, resampling, and state estimation.
The document provides details on a course calendar and lecture plan for hidden Markov models (HMM).
1) The course calendar covers topics like Bayesian estimation, Kalman filters, particle filters, hidden Markov models, supervised learning, and clustering algorithms over 14 weeks.
2) The HMM lecture plan introduces discrete-time HMMs and their applications. It covers the three main problems of HMMs - evaluation, decoding, and learning. Evaluation calculates the probability of an output sequence, decoding finds the most probable hidden state sequence, and learning estimates model parameters from training data.
3) The trellis diagram and forward algorithm are described for solving the evaluation problem, while the Viterbi and forward-backward algorithms are mentioned
When models are defined implicitly as systems of differential equations with no closed form solution, the choice of discretization grid for their approximation represents a trade-off between accuracy of the estimated solution and computational resources. We apply principles of statistical design to a class of sequential probability based models of discretization uncertainty for selecting the optimal discretization grid adaptively. Our proposal is compared to other approaches in the literature.
This document summarizes the MATLAB Reservoir Simulation Toolbox (MRST), which provides an environment for reservoir modelling and simulation using MATLAB. MRST features fully unstructured grids, rapid prototyping capabilities through automatic differentiation and object-oriented design, and industry-standard simulation methods. It has a large international user base in both academia and industry and consists of over 50 modules and thousands of lines of code.
This document discusses the use of Monte Carlo simulations to evaluate measurement uncertainty according to Supplement 1 to the Guide to the Expression of Uncertainty in Measurement (GUM+1). It provides examples of generating random numbers and using them in Monte Carlo simulations. One example evaluates the uncertainty in measuring the volume of a cylinder based on uncertainties in diameter and height measurements. The document concludes with an example of calibrating a thermometer and determining the uncertainties in the calibration curve parameters.
1) The document outlines the key steps and equations of the Kalman filter algorithm for optimal state estimation.
2) It describes the Kalman filter as a recursive algorithm that uses a system's dynamics model and noisy measurements to produce optimal estimates of unknown variables.
3) The algorithm involves two main steps - prediction using the system model to produce an estimate, and correction using new measurements to update the estimate.
This document discusses parameter estimation, model selection, and hypothesis testing for time series models. It begins by explaining maximum likelihood estimation for linear models like MA(1) and ARMA(1,1). Then it introduces the Akaike Information Criterion (AIC) for model selection, choosing the model with the minimum AIC value. Finally, it describes using hypothesis testing to determine which class an unknown signal belongs to by calculating the likelihood of the signal under models of each class. The document concludes by providing a demo example that estimates models for two classes of EEG data, selects the best model for each class using AIC, and applies hypothesis testing to determine the class of new test data.
Lec4 State Variable Models are used for modeingShehzadAhmed90
State variable models provide more internal information about a system compared to transfer function models, allowing for more complete control system design and analysis. The state of a system is defined as the minimum amount of information needed to uniquely determine the future behavior of the system given the inputs. State variable models are written in standard state space form with state, input, and output equations relating the state vector x, input vector u, and output vector y. An example RLC circuit is modeled using state space equations, and the solution is obtained using Laplace transforms.
The document describes Bayesian model updating research using adaptive Bayesian filters and data-centric approaches. It outlines previous contributions, future research plans, and short-term objectives. The focus is on Bayesian updating with MCMC and TMCMC approaches to more accurately and efficiently update model parameters. Model reduction techniques are proposed in the frequency domain and time domain to address incomplete measured responses. Numerical studies on a shear building model demonstrate that the Bayesian updating algorithm can estimate parameters well when using 45 data sets and hyperparameters of 0.001, 0.001, with a maximum error of 2.5%.
In these two lectures, we’re looking at basic discrete time representations of linear, time invariant plants and models and seeing how their parameters can be estimated using the normal equations.
The key example is the first order, linear, stable RC electrical circuit which we met last week, and which has an exponential response.
This document provides a summary of spatial data modeling and analysis techniques. It begins with an outline of the topics to be covered, including additive statistical models for spatial data, spatial covariance functions, the multivariate normal distribution, kriging for prediction and uncertainty, and the likelihood function for parameter estimation. It then introduces the key concepts and equations for modeling spatial processes as Gaussian random fields with specified covariance functions. Examples are given of commonly used covariance functions and the types of random surfaces they generate. Kriging is described as a best linear unbiased prediction technique that uses a spatial covariance function and observations to make predictions at unknown locations. The document concludes with examples of parameter estimation via maximum likelihood and using the fitted model to make predictions and conditional simulations
The document discusses various methods for developing empirical dynamic models from process input-output data, including linear regression and least squares estimation. Simple linear regression can be used to develop steady-state models relating an output variable y to an input variable u. The least squares approach is introduced to calculate the parameter estimates that minimize the error between measured and predicted output values. Graphical methods are also presented for estimating parameters of first-order and second-order dynamic models by fitting step response data. Finally, the development of discrete-time models from continuous-time models using finite difference approximations is covered.
The paper examines the problem of systems redesign within the context of passive electrical networks and through analogies provides also the means of addressing issues of re-design of mechanical networks. The problem addressed here are special cases of the more general network redesign problem. Redesigning autonomous passive electric networks involves changing the network natural dynamics by modification of the types of elements, possibly their values, interconnection topology and possibly addition, or elimination of parts of the network. We investigate the modelling of systems, whose structure is not fixed but evolves during the system lifecycle. As such, this is a problem that differs considerably from a standard control problem, since it involves changing the system itself without control and aims to achieve the desirable system properties, as these may be expressed by the natural frequencies by system re-engineering. In fact, this problem involves the selection of alternative values for dynamic elements and non-dynamic elements within a fixed interconnection topology and/or alteration of the network interconnection topology and possible evolution of the cardinality of physical elements (increase of elements, branches). The aim of the paper is to define an appropriate representation framework that allows the deployment of control theoretic tools for the re-engineering of properties of a given network. We use impedance and admittance modelling for passive electrical networks and develop a systems framework that is capable of addressing “life-cycle design issues” of networks where the problems of alteration of existing topology and values of the elements, as well as issues of growth, or death of parts of the network are addressed.
We use the Natural Impedance/ Admittance (NI-A) models and we establish a representation of the different types of transformations on such models. This representation provides the means for an appropriate formulation of natural frequencies assignment using the Determinantal Assignment Problem framework defined on appropriate structured transformations. The developed natural representation of transformations are expressed as additive structured transformations. For the simpler case of RL or RC networks it is shown that the single parameter variation problem (dynamic or non-dynamic) is equivalent to Root Locus problems.
follow IEEE NTUA SB on facebook:
https://www.facebook.com/IeeeNtuaSB
This paper deals with the problem of undesired memory effects in nonlinear digital filters owing to the influence of past excitations on future outputs. The nonlinearities under consideration cover the usual types of overflow arithmetic employed in practice. Based on the Hankel norm performance, a new criterion is proposed to ensure the reduction of undesired memory effects in digital filters with overflow arithmetic. In absence of external input, the nonexistence of overflow oscillations is also confirmed by the proposed criterion. A numerical example together with simulation result showing the effectiveness of the criterion is given.
This paper deals with the problem of undesired memory effects in nonlinear digital filters owing to the influence of past excitations on future outputs. The nonlinearities under consideration cover the usual types of overflow arithmetic employed in practice. Based on the Hankel norm performance, a new criterion is proposed to ensure the reduction of undesired memory effects in digital filters with overflow arithmetic. In absence of external input, the nonexistence of overflow oscillations is also confirmed by the proposed criterion. A numerical example together with simulation result showing the effectiveness of the criterion is given.
1) The document describes a fractional order nonlinear quarter car suspension model. It establishes integer and fractional order differential equations to model the system.
2) Key parameters of the suspension system are defined including mass, stiffness coefficients, and hysteretic nonlinear damping forces. State space and discrete forms of the fractional order model are presented.
3) Numerical methods for solving the fractional order differential equations are discussed, including the Adams-Bashforth-Moulton algorithm used to analyze the quarter car model. Stability of equilibrium points is analyzed.
The aim of this presentation is to revise the functional regression models with scalar response (Linear, Nonlinear and Semilinear) and the extension to the more general case where the response belongs to the exponential family (binomial, poisson, gamma, ...). This extension allows to develop new functional classification methods based on this regression models. Some examples along with code implementation in R are provided during the talk. Lecturer: Manuel Febrero Bande, Univ. de Santiago de Compostela, Spain.
Exploiting Hierarchy in the Abstraction-Based Verification of Statecharts Usi...Akos Hajdu
Presentation of our paper at the 14th International Workshop on Formal Engineering approaches to Software Components and Architectures (FESCA 2017). Uppsala, Sweden
This document describes the implementation of an Extended Kalman Filter (EKF) to estimate the state (position and heading angle) of a bicycle model. The EKF was able to provide reasonably accurate estimates of position over time based on position measurements and steering/velocity inputs, but struggled to accurately estimate the heading angle due to a lack of direct measurements. Histograms of the final state errors across many test cases showed normally distributed position errors and a uniformly distributed random heading angle error. While the EKF provided an approximation, a more advanced filter may have yielded better heading angle estimates.
The document provides a course calendar for a class on Bayesian estimation methods. It lists the dates and topics to be covered over 15 class periods from September to January. The topics progress from basic concepts like Bayes estimation and the Kalman filter, to more modern methods like particle filters, hidden Markov models, Bayesian decision theory, and applications of principal component analysis and independent component analysis. One class is noted as having no class.
Positive and negative solutions of a boundary value problem for a fractional ...journal ijrtem
: In this work, we study a boundary value problem for a fractional
q, -difference equation. By
using the monotone iterative technique and lower-upper solution method, we get the existence of positive or
negative solutions under the nonlinear term is local continuity and local monotonicity. The results show that we
can construct two iterative sequences for approximating the solutions
Facilitation Skills - When to Use and Why.pptxKnoldus Inc.
In this session, we will discuss the world of Agile methodologies and how facilitation plays a crucial role in optimizing collaboration, communication, and productivity within Scrum teams. We'll dive into the key facets of effective facilitation and how it can transform sprint planning, daily stand-ups, sprint reviews, and retrospectives. The participants will gain valuable insights into the art of choosing the right facilitation techniques for specific scenarios, aligning with Agile values and principles. We'll explore the "why" behind each technique, emphasizing the importance of adaptability and responsiveness in the ever-evolving Agile landscape. Overall, this session will help participants better understand the significance of facilitation in Agile and how it can enhance the team's productivity and communication.
An All-Around Benchmark of the DBaaS MarketScyllaDB
The entire database market is moving towards Database-as-a-Service (DBaaS), resulting in a heterogeneous DBaaS landscape shaped by database vendors, cloud providers, and DBaaS brokers. This DBaaS landscape is rapidly evolving and the DBaaS products differ in their features but also their price and performance capabilities. In consequence, selecting the optimal DBaaS provider for the customer needs becomes a challenge, especially for performance-critical applications.
To enable an on-demand comparison of the DBaaS landscape we present the benchANT DBaaS Navigator, an open DBaaS comparison platform for management and deployment features, costs, and performance. The DBaaS Navigator is an open data platform that enables the comparison of over 20 DBaaS providers for the relational and NoSQL databases.
This talk will provide a brief overview of the benchmarked categories with a focus on the technical categories such as price/performance for NoSQL DBaaS and how ScyllaDB Cloud is performing.
This document summarizes the MATLAB Reservoir Simulation Toolbox (MRST), which provides an environment for reservoir modelling and simulation using MATLAB. MRST features fully unstructured grids, rapid prototyping capabilities through automatic differentiation and object-oriented design, and industry-standard simulation methods. It has a large international user base in both academia and industry and consists of over 50 modules and thousands of lines of code.
This document discusses the use of Monte Carlo simulations to evaluate measurement uncertainty according to Supplement 1 to the Guide to the Expression of Uncertainty in Measurement (GUM+1). It provides examples of generating random numbers and using them in Monte Carlo simulations. One example evaluates the uncertainty in measuring the volume of a cylinder based on uncertainties in diameter and height measurements. The document concludes with an example of calibrating a thermometer and determining the uncertainties in the calibration curve parameters.
1) The document outlines the key steps and equations of the Kalman filter algorithm for optimal state estimation.
2) It describes the Kalman filter as a recursive algorithm that uses a system's dynamics model and noisy measurements to produce optimal estimates of unknown variables.
3) The algorithm involves two main steps - prediction using the system model to produce an estimate, and correction using new measurements to update the estimate.
This document discusses parameter estimation, model selection, and hypothesis testing for time series models. It begins by explaining maximum likelihood estimation for linear models like MA(1) and ARMA(1,1). Then it introduces the Akaike Information Criterion (AIC) for model selection, choosing the model with the minimum AIC value. Finally, it describes using hypothesis testing to determine which class an unknown signal belongs to by calculating the likelihood of the signal under models of each class. The document concludes by providing a demo example that estimates models for two classes of EEG data, selects the best model for each class using AIC, and applies hypothesis testing to determine the class of new test data.
Lec4 State Variable Models are used for modeingShehzadAhmed90
State variable models provide more internal information about a system compared to transfer function models, allowing for more complete control system design and analysis. The state of a system is defined as the minimum amount of information needed to uniquely determine the future behavior of the system given the inputs. State variable models are written in standard state space form with state, input, and output equations relating the state vector x, input vector u, and output vector y. An example RLC circuit is modeled using state space equations, and the solution is obtained using Laplace transforms.
The document describes Bayesian model updating research using adaptive Bayesian filters and data-centric approaches. It outlines previous contributions, future research plans, and short-term objectives. The focus is on Bayesian updating with MCMC and TMCMC approaches to more accurately and efficiently update model parameters. Model reduction techniques are proposed in the frequency domain and time domain to address incomplete measured responses. Numerical studies on a shear building model demonstrate that the Bayesian updating algorithm can estimate parameters well when using 45 data sets and hyperparameters of 0.001, 0.001, with a maximum error of 2.5%.
In these two lectures, we’re looking at basic discrete time representations of linear, time invariant plants and models and seeing how their parameters can be estimated using the normal equations.
The key example is the first order, linear, stable RC electrical circuit which we met last week, and which has an exponential response.
This document provides a summary of spatial data modeling and analysis techniques. It begins with an outline of the topics to be covered, including additive statistical models for spatial data, spatial covariance functions, the multivariate normal distribution, kriging for prediction and uncertainty, and the likelihood function for parameter estimation. It then introduces the key concepts and equations for modeling spatial processes as Gaussian random fields with specified covariance functions. Examples are given of commonly used covariance functions and the types of random surfaces they generate. Kriging is described as a best linear unbiased prediction technique that uses a spatial covariance function and observations to make predictions at unknown locations. The document concludes with examples of parameter estimation via maximum likelihood and using the fitted model to make predictions and conditional simulations
The document discusses various methods for developing empirical dynamic models from process input-output data, including linear regression and least squares estimation. Simple linear regression can be used to develop steady-state models relating an output variable y to an input variable u. The least squares approach is introduced to calculate the parameter estimates that minimize the error between measured and predicted output values. Graphical methods are also presented for estimating parameters of first-order and second-order dynamic models by fitting step response data. Finally, the development of discrete-time models from continuous-time models using finite difference approximations is covered.
The paper examines the problem of systems redesign within the context of passive electrical networks and through analogies provides also the means of addressing issues of re-design of mechanical networks. The problem addressed here are special cases of the more general network redesign problem. Redesigning autonomous passive electric networks involves changing the network natural dynamics by modification of the types of elements, possibly their values, interconnection topology and possibly addition, or elimination of parts of the network. We investigate the modelling of systems, whose structure is not fixed but evolves during the system lifecycle. As such, this is a problem that differs considerably from a standard control problem, since it involves changing the system itself without control and aims to achieve the desirable system properties, as these may be expressed by the natural frequencies by system re-engineering. In fact, this problem involves the selection of alternative values for dynamic elements and non-dynamic elements within a fixed interconnection topology and/or alteration of the network interconnection topology and possible evolution of the cardinality of physical elements (increase of elements, branches). The aim of the paper is to define an appropriate representation framework that allows the deployment of control theoretic tools for the re-engineering of properties of a given network. We use impedance and admittance modelling for passive electrical networks and develop a systems framework that is capable of addressing “life-cycle design issues” of networks where the problems of alteration of existing topology and values of the elements, as well as issues of growth, or death of parts of the network are addressed.
We use the Natural Impedance/ Admittance (NI-A) models and we establish a representation of the different types of transformations on such models. This representation provides the means for an appropriate formulation of natural frequencies assignment using the Determinantal Assignment Problem framework defined on appropriate structured transformations. The developed natural representation of transformations are expressed as additive structured transformations. For the simpler case of RL or RC networks it is shown that the single parameter variation problem (dynamic or non-dynamic) is equivalent to Root Locus problems.
follow IEEE NTUA SB on facebook:
https://www.facebook.com/IeeeNtuaSB
This paper deals with the problem of undesired memory effects in nonlinear digital filters owing to the influence of past excitations on future outputs. The nonlinearities under consideration cover the usual types of overflow arithmetic employed in practice. Based on the Hankel norm performance, a new criterion is proposed to ensure the reduction of undesired memory effects in digital filters with overflow arithmetic. In absence of external input, the nonexistence of overflow oscillations is also confirmed by the proposed criterion. A numerical example together with simulation result showing the effectiveness of the criterion is given.
This paper deals with the problem of undesired memory effects in nonlinear digital filters owing to the influence of past excitations on future outputs. The nonlinearities under consideration cover the usual types of overflow arithmetic employed in practice. Based on the Hankel norm performance, a new criterion is proposed to ensure the reduction of undesired memory effects in digital filters with overflow arithmetic. In absence of external input, the nonexistence of overflow oscillations is also confirmed by the proposed criterion. A numerical example together with simulation result showing the effectiveness of the criterion is given.
1) The document describes a fractional order nonlinear quarter car suspension model. It establishes integer and fractional order differential equations to model the system.
2) Key parameters of the suspension system are defined including mass, stiffness coefficients, and hysteretic nonlinear damping forces. State space and discrete forms of the fractional order model are presented.
3) Numerical methods for solving the fractional order differential equations are discussed, including the Adams-Bashforth-Moulton algorithm used to analyze the quarter car model. Stability of equilibrium points is analyzed.
The aim of this presentation is to revise the functional regression models with scalar response (Linear, Nonlinear and Semilinear) and the extension to the more general case where the response belongs to the exponential family (binomial, poisson, gamma, ...). This extension allows to develop new functional classification methods based on this regression models. Some examples along with code implementation in R are provided during the talk. Lecturer: Manuel Febrero Bande, Univ. de Santiago de Compostela, Spain.
Exploiting Hierarchy in the Abstraction-Based Verification of Statecharts Usi...Akos Hajdu
Presentation of our paper at the 14th International Workshop on Formal Engineering approaches to Software Components and Architectures (FESCA 2017). Uppsala, Sweden
This document describes the implementation of an Extended Kalman Filter (EKF) to estimate the state (position and heading angle) of a bicycle model. The EKF was able to provide reasonably accurate estimates of position over time based on position measurements and steering/velocity inputs, but struggled to accurately estimate the heading angle due to a lack of direct measurements. Histograms of the final state errors across many test cases showed normally distributed position errors and a uniformly distributed random heading angle error. While the EKF provided an approximation, a more advanced filter may have yielded better heading angle estimates.
The document provides a course calendar for a class on Bayesian estimation methods. It lists the dates and topics to be covered over 15 class periods from September to January. The topics progress from basic concepts like Bayes estimation and the Kalman filter, to more modern methods like particle filters, hidden Markov models, Bayesian decision theory, and applications of principal component analysis and independent component analysis. One class is noted as having no class.
Positive and negative solutions of a boundary value problem for a fractional ...journal ijrtem
: In this work, we study a boundary value problem for a fractional
q, -difference equation. By
using the monotone iterative technique and lower-upper solution method, we get the existence of positive or
negative solutions under the nonlinear term is local continuity and local monotonicity. The results show that we
can construct two iterative sequences for approximating the solutions
Facilitation Skills - When to Use and Why.pptxKnoldus Inc.
In this session, we will discuss the world of Agile methodologies and how facilitation plays a crucial role in optimizing collaboration, communication, and productivity within Scrum teams. We'll dive into the key facets of effective facilitation and how it can transform sprint planning, daily stand-ups, sprint reviews, and retrospectives. The participants will gain valuable insights into the art of choosing the right facilitation techniques for specific scenarios, aligning with Agile values and principles. We'll explore the "why" behind each technique, emphasizing the importance of adaptability and responsiveness in the ever-evolving Agile landscape. Overall, this session will help participants better understand the significance of facilitation in Agile and how it can enhance the team's productivity and communication.
An All-Around Benchmark of the DBaaS MarketScyllaDB
The entire database market is moving towards Database-as-a-Service (DBaaS), resulting in a heterogeneous DBaaS landscape shaped by database vendors, cloud providers, and DBaaS brokers. This DBaaS landscape is rapidly evolving and the DBaaS products differ in their features but also their price and performance capabilities. In consequence, selecting the optimal DBaaS provider for the customer needs becomes a challenge, especially for performance-critical applications.
To enable an on-demand comparison of the DBaaS landscape we present the benchANT DBaaS Navigator, an open DBaaS comparison platform for management and deployment features, costs, and performance. The DBaaS Navigator is an open data platform that enables the comparison of over 20 DBaaS providers for the relational and NoSQL databases.
This talk will provide a brief overview of the benchmarked categories with a focus on the technical categories such as price/performance for NoSQL DBaaS and how ScyllaDB Cloud is performing.
Communications Mining Series - Zero to Hero - Session 2DianaGray10
This session is focused on setting up Project, Train Model and Refine Model in Communication Mining platform. We will understand data ingestion, various phases of Model training and best practices.
• Administration
• Manage Sources and Dataset
• Taxonomy
• Model Training
• Refining Models and using Validation
• Best practices
• Q/A
Must Know Postgres Extension for DBA and Developer during MigrationMydbops
Mydbops Opensource Database Meetup 16
Topic: Must-Know PostgreSQL Extensions for Developers and DBAs During Migration
Speaker: Deepak Mahto, Founder of DataCloudGaze Consulting
Date & Time: 8th June | 10 AM - 1 PM IST
Venue: Bangalore International Centre, Bangalore
Abstract: Discover how PostgreSQL extensions can be your secret weapon! This talk explores how key extensions enhance database capabilities and streamline the migration process for users moving from other relational databases like Oracle.
Key Takeaways:
* Learn about crucial extensions like oracle_fdw, pgtt, and pg_audit that ease migration complexities.
* Gain valuable strategies for implementing these extensions in PostgreSQL to achieve license freedom.
* Discover how these key extensions can empower both developers and DBAs during the migration process.
* Don't miss this chance to gain practical knowledge from an industry expert and stay updated on the latest open-source database trends.
Mydbops Managed Services specializes in taking the pain out of database management while optimizing performance. Since 2015, we have been providing top-notch support and assistance for the top three open-source databases: MySQL, MongoDB, and PostgreSQL.
Our team offers a wide range of services, including assistance, support, consulting, 24/7 operations, and expertise in all relevant technologies. We help organizations improve their database's performance, scalability, efficiency, and availability.
Contact us: info@mydbops.com
Visit: https://www.mydbops.com/
Follow us on LinkedIn: https://in.linkedin.com/company/mydbops
For more details and updates, please follow up the below links.
Meetup Page : https://www.meetup.com/mydbops-databa...
Twitter: https://twitter.com/mydbopsofficial
Blogs: https://www.mydbops.com/blog/
Facebook(Meta): https://www.facebook.com/mydbops/
Introducing BoxLang : A new JVM language for productivity and modularity!Ortus Solutions, Corp
Just like life, our code must adapt to the ever changing world we live in. From one day coding for the web, to the next for our tablets or APIs or for running serverless applications. Multi-runtime development is the future of coding, the future is to be dynamic. Let us introduce you to BoxLang.
Dynamic. Modular. Productive.
BoxLang redefines development with its dynamic nature, empowering developers to craft expressive and functional code effortlessly. Its modular architecture prioritizes flexibility, allowing for seamless integration into existing ecosystems.
Interoperability at its Core
With 100% interoperability with Java, BoxLang seamlessly bridges the gap between traditional and modern development paradigms, unlocking new possibilities for innovation and collaboration.
Multi-Runtime
From the tiny 2m operating system binary to running on our pure Java web server, CommandBox, Jakarta EE, AWS Lambda, Microsoft Functions, Web Assembly, Android and more. BoxLang has been designed to enhance and adapt according to it's runnable runtime.
The Fusion of Modernity and Tradition
Experience the fusion of modern features inspired by CFML, Node, Ruby, Kotlin, Java, and Clojure, combined with the familiarity of Java bytecode compilation, making BoxLang a language of choice for forward-thinking developers.
Empowering Transition with Transpiler Support
Transitioning from CFML to BoxLang is seamless with our JIT transpiler, facilitating smooth migration and preserving existing code investments.
Unlocking Creativity with IDE Tools
Unleash your creativity with powerful IDE tools tailored for BoxLang, providing an intuitive development experience and streamlining your workflow. Join us as we embark on a journey to redefine JVM development. Welcome to the era of BoxLang.
An Introduction to All Data Enterprise IntegrationSafe Software
Are you spending more time wrestling with your data than actually using it? You’re not alone. For many organizations, managing data from various sources can feel like an uphill battle. But what if you could turn that around and make your data work for you effortlessly? That’s where FME comes in.
We’ve designed FME to tackle these exact issues, transforming your data chaos into a streamlined, efficient process. Join us for an introduction to All Data Enterprise Integration and discover how FME can be your game-changer.
During this webinar, you’ll learn:
- Why Data Integration Matters: How FME can streamline your data process.
- The Role of Spatial Data: Why spatial data is crucial for your organization.
- Connecting & Viewing Data: See how FME connects to your data sources, with a flash demo to showcase.
- Transforming Your Data: Find out how FME can transform your data to fit your needs. We’ll bring this process to life with a demo leveraging both geometry and attribute validation.
- Automating Your Workflows: Learn how FME can save you time and money with automation.
Don’t miss this chance to learn how FME can bring your data integration strategy to life, making your workflows more efficient and saving you valuable time and resources. Join us and take the first step toward a more integrated, efficient, data-driven future!
ScyllaDB Real-Time Event Processing with CDCScyllaDB
ScyllaDB’s Change Data Capture (CDC) allows you to stream both the current state as well as a history of all changes made to your ScyllaDB tables. In this talk, Senior Solution Architect Guilherme Nogueira will discuss how CDC can be used to enable Real-time Event Processing Systems, and explore a wide-range of integrations and distinct operations (such as Deltas, Pre-Images and Post-Images) for you to get started with it.
So You've Lost Quorum: Lessons From Accidental DowntimeScyllaDB
The best thing about databases is that they always work as intended, and never suffer any downtime. You'll never see a system go offline because of a database outage. In this talk, Bo Ingram -- staff engineer at Discord and author of ScyllaDB in Action --- dives into an outage with one of their ScyllaDB clusters, showing how a stressed ScyllaDB cluster looks and behaves during an incident. You'll learn about how to diagnose issues in your clusters, see how external failure modes manifest in ScyllaDB, and how you can avoid making a fault too big to tolerate.
QR Secure: A Hybrid Approach Using Machine Learning and Security Validation F...AlexanderRichford
QR Secure: A Hybrid Approach Using Machine Learning and Security Validation Functions to Prevent Interaction with Malicious QR Codes.
Aim of the Study: The goal of this research was to develop a robust hybrid approach for identifying malicious and insecure URLs derived from QR codes, ensuring safe interactions.
This is achieved through:
Machine Learning Model: Predicts the likelihood of a URL being malicious.
Security Validation Functions: Ensures the derived URL has a valid certificate and proper URL format.
This innovative blend of technology aims to enhance cybersecurity measures and protect users from potential threats hidden within QR codes 🖥 🔒
This study was my first introduction to using ML which has shown me the immense potential of ML in creating more secure digital environments!
The Department of Veteran Affairs (VA) invited Taylor Paschal, Knowledge & Information Management Consultant at Enterprise Knowledge, to speak at a Knowledge Management Lunch and Learn hosted on June 12, 2024. All Office of Administration staff were invited to attend and received professional development credit for participating in the voluntary event.
The objectives of the Lunch and Learn presentation were to:
- Review what KM ‘is’ and ‘isn’t’
- Understand the value of KM and the benefits of engaging
- Define and reflect on your “what’s in it for me?”
- Share actionable ways you can participate in Knowledge - - Capture & Transfer
Day 4 - Excel Automation and Data ManipulationUiPathCommunity
👉 Check out our full 'Africa Series - Automation Student Developers (EN)' page to register for the full program: https://bit.ly/Africa_Automation_Student_Developers
In this fourth session, we shall learn how to automate Excel-related tasks and manipulate data using UiPath Studio.
📕 Detailed agenda:
About Excel Automation and Excel Activities
About Data Manipulation and Data Conversion
About Strings and String Manipulation
💻 Extra training through UiPath Academy:
Excel Automation with the Modern Experience in Studio
Data Manipulation with Strings in Studio
👉 Register here for our upcoming Session 5/ June 25: Making Your RPA Journey Continuous and Beneficial: https://community.uipath.com/events/details/uipath-lagos-presents-session-5-making-your-automation-journey-continuous-and-beneficial/
ScyllaDB is making a major architecture shift. We’re moving from vNode replication to tablets – fragments of tables that are distributed independently, enabling dynamic data distribution and extreme elasticity. In this keynote, ScyllaDB co-founder and CTO Avi Kivity explains the reason for this shift, provides a look at the implementation and roadmap, and shares how this shift benefits ScyllaDB users.
Enterprise Knowledge’s Joe Hilger, COO, and Sara Nash, Principal Consultant, presented “Building a Semantic Layer of your Data Platform” at Data Summit Workshop on May 7th, 2024 in Boston, Massachusetts.
This presentation delved into the importance of the semantic layer and detailed four real-world applications. Hilger and Nash explored how a robust semantic layer architecture optimizes user journeys across diverse organizational needs, including data consistency and usability, search and discovery, reporting and insights, and data modernization. Practical use cases explore a variety of industries such as biotechnology, financial services, and global retail.
Northern Engraving | Modern Metal Trim, Nameplates and Appliance PanelsNorthern Engraving
What began over 115 years ago as a supplier of precision gauges to the automotive industry has evolved into being an industry leader in the manufacture of product branding, automotive cockpit trim and decorative appliance trim. Value-added services include in-house Design, Engineering, Program Management, Test Lab and Tool Shops.
Radically Outperforming DynamoDB @ Digital Turbine with SADA and Google CloudScyllaDB
Digital Turbine, the Leading Mobile Growth & Monetization Platform, did the analysis and made the leap from DynamoDB to ScyllaDB Cloud on GCP. Suffice it to say, they stuck the landing. We'll introduce Joseph Shorter, VP, Platform Architecture at DT, who lead the charge for change and can speak first-hand to the performance, reliability, and cost benefits of this move. Miles Ward, CTO @ SADA will help explore what this move looks like behind the scenes, in the Scylla Cloud SaaS platform. We'll walk you through before and after, and what it took to get there (easier than you'd guess I bet!).
This time, we're diving into the murky waters of the Fuxnet malware, a brainchild of the illustrious Blackjack hacking group.
Let's set the scene: Moscow, a city unsuspectingly going about its business, unaware that it's about to be the star of Blackjack's latest production. The method? Oh, nothing too fancy, just the classic "let's potentially disable sensor-gateways" move.
In a move of unparalleled transparency, Blackjack decides to broadcast their cyber conquests on ruexfil.com. Because nothing screams "covert operation" like a public display of your hacking prowess, complete with screenshots for the visually inclined.
Ah, but here's where the plot thickens: the initial claim of 2,659 sensor-gateways laid to waste? A slight exaggeration, it seems. The actual tally? A little over 500. It's akin to declaring world domination and then barely managing to annex your backyard.
For Blackjack, ever the dramatists, hint at a sequel, suggesting the JSON files were merely a teaser of the chaos yet to come. Because what's a cyberattack without a hint of sequel bait, teasing audiences with the promise of more digital destruction?
-------
This document presents a comprehensive analysis of the Fuxnet malware, attributed to the Blackjack hacking group, which has reportedly targeted infrastructure. The analysis delves into various aspects of the malware, including its technical specifications, impact on systems, defense mechanisms, propagation methods, targets, and the motivations behind its deployment. By examining these facets, the document aims to provide a detailed overview of Fuxnet's capabilities and its implications for cybersecurity.
The document offers a qualitative summary of the Fuxnet malware, based on the information publicly shared by the attackers and analyzed by cybersecurity experts. This analysis is invaluable for security professionals, IT specialists, and stakeholders in various industries, as it not only sheds light on the technical intricacies of a sophisticated cyber threat but also emphasizes the importance of robust cybersecurity measures in safeguarding critical infrastructure against emerging threats. Through this detailed examination, the document contributes to the broader understanding of cyber warfare tactics and enhances the preparedness of organizations to defend against similar attacks in the future.
In our second session, we shall learn all about the main features and fundamentals of UiPath Studio that enable us to use the building blocks for any automation project.
📕 Detailed agenda:
Variables and Datatypes
Workflow Layouts
Arguments
Control Flows and Loops
Conditional Statements
💻 Extra training through UiPath Academy:
Variables, Constants, and Arguments in Studio
Control Flow in Studio
Automation Student Developers Session 3: Introduction to UI AutomationUiPathCommunity
👉 Check out our full 'Africa Series - Automation Student Developers (EN)' page to register for the full program: http://bit.ly/Africa_Automation_Student_Developers
After our third session, you will find it easy to use UiPath Studio to create stable and functional bots that interact with user interfaces.
📕 Detailed agenda:
About UI automation and UI Activities
The Recording Tool: basic, desktop, and web recording
About Selectors and Types of Selectors
The UI Explorer
Using Wildcard Characters
💻 Extra training through UiPath Academy:
User Interface (UI) Automation
Selectors in Studio Deep Dive
👉 Register here for our upcoming Session 4/June 24: Excel Automation and Data Manipulation: https://community.uipath.com/events/details
Automation Student Developers Session 3: Introduction to UI Automation
Vu_HPSC2012_02.pptx
1. An Improved
Direct Multiple Shooting Approach
Combined with Collocation & Parallel Computing
to Handle Path Constraints in
Dynamic Nonlinear Optimization
Simulation and Optimal Processes (SOP) Group
Ilmenau University of Technology
Quoc Dong Vu & Pu Li
2. 2
Outline
• Dynamic Nonlinear Optimization and Direct
Multiple Shooting combined with Collocation
• Combined Approach DMS with Collocation
• Parallel computing
• Case studies
• Conclusions and future work
3. 3
Dynamic Nonlinear Optimization
, ,
0
0
min ( ), ( ), (1.a)
. . , ( ), ( ), 0 (1.b)
( ), ( ), 0 (1.c)
( ) (1.d)
( ) (1.e)
(1.f )
0 (1.g)
[ , ]
x u p
L U
L U
L U
f
x t u t p
s t F x x t u t p
G x t u t p
x x t x
u u t u
p p p
x x
t t t
F: vector of differential equations
G: vector of algebraic equations
x(t): state (dependent) variables
u(t): input (independent) variables
p: system parameters
x0: initial conditions of state variables
Constrained dynamic optimization problem:
4. 4
Nonlinear Dynamic
Optimization (DNO)
DMS scheme
Discretizing time horizon, parametrizing controls
and intial conditions on each subinterval.
Nonlinear Programing
Problem (NLP)
Direct Multiple Shooting (DMS)
t0 t1 t2 t3 t4=tf
u1
u0 u2 u3
x0
x0,1 x0,2 x0,3
p
uupper
xupper
ulower
xlower
t0 t1 t2 t3
u1
u0
u2
u3
x0
p
uupper
xupper
ulower
xlower
t4=tf
5. 5
Collocation on finite
elements (CFE):
Combined Approach DMS
with Collocation
0 0
1
, , 0
NC
j i j i i i
j
T t x t T t x t f x t u t t
ODE Solution:
0
0
( ) ( )
( )
NC
j i j
j
NC
k
j
j j k
j k
x T t x t
t t
T t
t t
• State trajectories
• Sensitivities computation
State path constraints
can be satisfied on inner
collocation points
6. are computed here
by simulation task
independently for
each time interval,
so parallel
computing is
applicable.
6
0
0
, ,
0
, 1,0
min ( , , ), , (2.a)
. . ( , , ), , 0 (2.b)
(2.c)
(2.d)
(2.e)
(2.f)
1,...,
NC:Numberof collocation points
NL: Numberof timeintervals
x u p
i
L U
L U
L U
i NC i
x x u p u p
s t c x x u p u p
x x x
u u u
p p p
x x
i NL
After discretization:
0
, , ,
dx dx dx
x
dx du dp
Path
constraints
handled for
all
collocation
points
Combined Approach using DMS
with Collocation
7. 7
SQP based optimization method
Solving model
equations with
Newton method
Calculation of
gradients
Middle stage
Lower stage
Optimization Layer
Simulation Layer
Value of
State variables
Gradients
IP based optimization method
Solving model
equations with
Newton method
(MS & collocation)
Calculation of
gradients
Optimization Layer
Simulation Layer
Values of
state
variables
Controls u, Parameters p
Initial x0
Combined Approach using DMS
with Collocation
8. 8
Simulation layer
• ci in each time interval is
independent from others,
so parallel programming
with MPI or/and
OpenMP can be applied.
• Employ Newton to solve model equations at each
time interval:
0
( , , ), , 0 (3)
1,..., ;
i
c x x u p u p
i NL
8
Combined Approach DMS
with Collocation
State path constraints
on inner collocation
points will be held.
t0 t1 t2 t3 t4=tf
x0
p
uupper
xupper
ulower
xlower
0
u 1
u 2
u 3
u
0,1
x 0,2
x 0,3
x
,0
NC
x ,1
NC
x ,2
NC
x ,3
NC
x
9. 9
Optimization layer:
9
0
0
, ,
, 1,0
min ( , , ), , (4.a)
(4.b)
(4.c)
(4.d)
(4.e)
1,...,
NC:Numberof collocation points
NL: Numberof timeintervals
x u p
L U
L U
L U
i NC i
x x u p u p
x x x
u u u
p p p
x x
i NL
Combined Approach DMS
with Collocation
at all collocation points
x
piecewise polynomials/constants
u
constants over time horizon
p
Continuity constraints
11. • Problem formulation:
3
2
1 2 1 2
2 1
2 2 2
3 1 2
1
min ( ) (5.a)
. . (1 ) (5.b)
(5.c)
(5.d)
( ) 0.5 (5.e)
0.3 ( ) 1.0 (5.f)
(0) [0, 1, 0] (5.g)
5.0 (5.h)
f
u
T
f
x t
s t x x x x u
x x
x x x u
x t
u t
x
t
11
Case studies:
control of Van der Pol Oscillator
* http://en.wikipedia.org/wiki/File:VanDerPolOscillator.png
Phase portrait of the
unforced Van der Pol
oscillator *
13. Case studies: Control of a CSTR
• Problem formulation:
13
2 2 2 2
1 1 2 2 1 1 2 2
,
0
0 1
1 2
0 0 2
2 0 2
2
1 3
0 0 3
3 0 2 2 3
2
1 3
1
min ( ) 100.0( ) 0.1( ) 0.1( ) (6.a)
. . (6.b)
exp( ) (6.c)
( ) 2
exp( ) ( )
0.5 2
(6
.
.d)
5 ; 0.87
f
t
s s s s
x u
p p
F x x x x u u u u dt
F u
s t x
r
F c x E
x k x
r x Rx
F T x H E U
x k x u x
r x C Rx r C
x m
2 3
1 2
1.0 / ; 290 350 ( )
85 115 / min ; 290 310 ( )
(0) [0.659, 0.877, 324.5] ( )
50.0(min)
6.e
6.f
6.g
f
x mol l x K
u l u K
x
t
* Source: http://upload.wikimedia.org/wikipedia/commons/thumb/b/be/Agitated_vessel.svg/408px-Agitated_vessel.svg.png
Cross-sectional
diagram of CSTR*
14. Case studies: CPU time
• CPU time:
14
CPU Time (ms)
Van der Pol Oscillator CSTR
without inner
point
constraints
with inner
point
constraints
with inner point
constraints
31 46
Serial Parallel
172 203
• CPU time with inner point constraints is longer
than the case without these constraints.
• CPU time with parallel computing in CSTR is
longer than serial case, since the time of data
transfer between threads is longer than
computing time.
15. 15
Errors-In-Variables (EIV) formulation:
0 ,0
-1
, , , -1
1 1 1
min (7.a)
. .
(7.b)
(7.c)
(7.d)
(7.e)
, , , , , 0
, , , 0
, , , 0
(7
j j j
T
M M
NS NS NK
j i j i y j i j i
j T
p u x y M M
j j i
j i j i u
j j j j j
j j j
j j
j i j i
L
j
j
U
j
y t y t W y t y t
F
u t u t W u t u t
s
f x t x t y t y t u t p
g x t y t u t p
h x t y t u t p
x t x
t
p p p
.f)
x(t): Unmeasured state variables
y(t): Measured state variables
yM (t), Measurements of output
uM (t): and input variables
h: Process restrictions
Wy,Wu: Known covariance matrices
NS: Number of data sets
NK: Number of measurement
points
Case studies:
parameter estimation problem
16. 16
Discretization with collocation on finite elements:
Note: It is assumed that the measurement points
coincide with the element positions.
, , , ,0 , , ,0
-1
, , ,0 , , , , ,0 , ,
, , , -1
1 1 1 1
, , ,
, , , , ,
, , , , ,
,
min (8.a)
, , (8.b
,
. ,
. )
0
, ,
j l j l i j l i
T
M M
NS NS NL NY
j l i j l i y j l i j l i
j j l i j l i j l
j j l i j l i
j T
p u x y M M
j j l i
j l
j l j l u j l j l
y y W y y
F F
u u W u u
g x y u p
h x
t
p
s
y u
(8.c)
(8.d)
wh
0
ere:
L U
j
j
j
p p p
f
g
g
Case studies:
parameter estimation problem
17. 1 1
2 2
3
1
1 1 1
1
1
2
2 2 2
2
2 1
3
3 3 3
3
3
3 2
4 1
1 4 1
4 1
5 2
1 5 2
5 2
6 3
1 6
5 3
(9.a)
1
(9.b)
1
(9.c)
1
(9.d)
(9.e)
ni na
ni na
ni na
V
G k G
Ka
P
Ki S
V
G k G
Ka
P
Ki M
V
G k G
Ka
P
Ki M
V G
E k E
K G
V G
E k E
K G
V G
E k E
K G
3
2 2 1 2
1 1 1
3
1
1
1 1 2
1 2 3 4
2 2 1 2 3 3 2
3 5
2
1 2 2
3 4 5 6
(9.f )
1
1
( )( )
( )( )
(9.g)
1 1
1 1
( )( ) ( )( )
(9.h)
1 1
kcat E M M
kcat E S M
Km
Km
M
M M M
S
Km Km Km Km
kcat E M M kcat E M P
Km Km
M
M M M P
Km Km Km Km
• The three-step
pathway parameters
estimation [3, 4]:
17
• 8 differential variables
of concentrations of
species in biochemical
reactions.
• 36 parameters need to
be estimated.
• 16 measured data
profiles.
Case studies:
parameter estimation problem
18. 18
AMIGO results DMS-COL results
Results with 36 parameters in high initials case:
Case studies:
parameter estimation problem
19. 19
Results with 36 parameters in low initials case:
AMIGO results DMS-COL results
Case studies:
parameter estimation problem
20. 20
The correlation matrix with 36 parameters (Source: AMIGO)
Case studies:
parameter estimation problem
22. 22
CPU time comparison
Case studies:
parameter estimation problem
CPU time (s)
AMIGO DMS-COL
Low High Low High
Serial Parallel Serial Parallel
20.9 19.3 51.6 44.5 IPOPT
48.7 8.8 196.8 24.7 Func.
146.8 127.9 69.6 28.1 248.4 69.2 Total
Intel Core i7-980, 6x 3.33GHz, 4GB RAM, Windows XP
23. Conclusions and future works
23
Conclusions
• The proposed DMS-COL method considers
state path constraints on inner collocation
points.
• Parallel computing efficiency depends on the
data transfer time between threads and
computing time of each thread.
Future works
• Study parallel computation with two stages.
• Test with more complicated examples.
24. Literature References
References
1. Bock, H. and Plitt, K. A multiple shooting algorithm for direct solution
of optimal control problems . In 9th IFAC World Congress, 1984,
242–247.
2. J. Tamimi and P. Li. A combined approach to nonlinear model
predictive control of fast systems, Journal of Process Control, 20,
2010, 1092-1102.
3. Eva Balsa-Canto and Julio R. Banga. AMIGO, a toolbox for
advanced model identification in systems biology using global
optimization. BIOINFORMATICS APPLICATIONS NOTE. Vol. 27 no.
16, 2011, 2311–2313.
4. Rodriguez-Fernandez M., Mendes P., Banga JR. A hybrid approach
for efficient and robust parameter estimation in biochemical pathways.
BioSystems, 83, 2006, 248-265.
24