Fuzzy Logic and Neural Networks

The paper presents an advanced control strategy that uses the neural network predictive controller and the fuzzy controller in the complex control structure with an auxiliary manipulated variable. The controlled tubular heat exchanger is used for pre-heating of petroleum by hot water. The heat exchanger is modelled as a nonlinear system with the interval parametric uncertainty. The set point tracking and the disturbance rejection using intelligent control strategies are investigated. The control objective is to keep the outlet temperature of the pre-heated petroleum at a reference value. Simulations of control of the tubular heat exchanger are done in the Matlab/Stimulant environment. The complex control structure with two controllers is compared with the conventional PID control, fuzzy control and NNPC. Simulation results confirm the effectiveness and superiority of the complex control structure combining the NNPC with the auxiliary fuzzy controller.

Scope of the book:
This book focusses on the technical concepts of deep learning and its associated branch Neural Networks for the various dimensions of image processing applications. The proposed volume intends to bring together researchers to report the latest results or progress in the development of the above-mentioned areas. Since there is a deficit of books on this specific subject matter, the editors aim to provide a common platform for researchers working in this area to exhibit their novel findings.
Topics of Interest:
This book solicits contributions, which include the fundamentals in the field of Deep Artificial Neural Networks and Image Processing supported by case studies and practical examples. Each chapter is expected to be self-contained and to cover an in-depth analysis of real life applications of neural networks to image analysis.

Multilabel Image Tagging is one of the most important challenges in computer vision with many real world applications and thus we have used Deep Neural Networks for Image Annotation to boost performance. This experiment is performed on NUS-WIDE Dataset with 1K Tags. I. INTRODUCTION Multilabel image annotation is an important and challenging problem in computer vision. Most existing work focus on single-label classification problems, where each image is assumed to have only one class label. However, this is not necessarily true for real world applications, as an image may be associated with multiple tags. As a practical example, images from Flickr are used as an example which has multiple tags, such as objects, activities, and scene descriptions. Images on the Internet, in general, are usually associated with sentences or descriptions, instead of a single class label, which is a type of multitagging. Therefore, it is a practical and important problem to accurately assign multiple labels to one image. Single-label image classification has been extensively studied in the vision community, the most recent advances reported on the large-scale ImageNet Architecture. Most existing work focus on designing visual features for improving recognition accuracy. For example, sparse coding, Fisher vectors, and VLAD have been proposed to reduce the quantization error of " bag of words "-type features. Very recently, deep convolutional neural networks (CNN) have demonstrated promising results for single-label image classification. Such algorithms have focused on one vs all, but one have worked on the multilabel image annotation problem. In this work, we used the highly expressive convolutional network for the problem of multilabel image annotation. We employed a similar network structure to as used in Image Net, which contains several convolutional and dense connected layers as the basic architecture. We studied and compared several other popular multilabel losses, such as the ranking loss that optimizes the area under ROC curve (AUC), and the cross-entropy loss used in Tagprop. Specifically, we propose to use the top-k ranking loss, inspired by, for embedding to train the network. Using the largest publicly available multilabel dataset NUS-WIDE, we observe a significant performance boost over conventional features, reporting the best retrieval performance. We even performed test on Triplet Loss giving much better results. II. MOTIVATION Multilabel image annotation is an important and challenging problem in computer vision. Most existing work focus on single-label classification problems, where each image is assumed to have only one class label. However, this is not necessarily true for real world applications, as an image may be associated with multiple tags. As a practical example, images from Flickr are used as an example which has multiple tags, such as objects, activities, and scene descriptions. Images on the Internet, in general, are usually associated with sentences or descriptions, instead of a single class label, which is a type of multitagging. Therefore, it is a practical and important problem to accurately assign multiple labels to one image.

This paper describes comparative study of various controllers on Rotary Inverted Pendulum (RIP). PID,
LQR, FUZZY LOGIC and H∞ controllers are tried on RIP in MatLab Simulink. The same four controllers
have been tested on test bed of RIP system the controllers are compared from various aspects. The
controllers in simulink are compared with the controllers in real time.

The paper presents an advanced control strategy that uses the neural network predictive controller and the fuzzy controller in the complex control structure with an auxiliary control variable. The controlled tubular heat exchanger was used for pre-heating of petroleum by hot water. The heat exchanger was modelled as a nonlinear system with interval parametric uncertainty. The set point tracking and the disturbance rejection using intelligent control strategies were investigated. The control objective was to keep the outlet temperature of the pre-heated petroleum at a reference value. Simulations of control of the tubular heat exchanger were done in the Matlab/Simulink environment. The neural network predictive control (NNPC) with fuzzy controller was compared with classical PID control. Simulation results obtained using NNPC with fuzzy controller and those obtained by classical PID control confirmed the effectiveness and superiority of the presented advanced control approach.

Fuzzy logic is highly appropriate and valid basis for developing knowledge-based systems in medicine for different tasks and it has been known to produce highly accurate results. Examples of such tasks include syndrome differentiation, likelihood survival for sickle cell anaemia among paediatric patients, diagnosis and optimal selection of medical treatments and real time monitoring of patients. For this paper, a Fuzzy logic-based system is untaken used to provide a comprehensive simulation of a prediction model for determining the likelihood of detecting Chronic Kidney failure/diseases in humans. The Fuzzy-based system uses a 4-tuple record comprising of the following test taken: Blood Urea Test, Urea Clearance Test, Creatinine Clearance test and Estimated Glomerular Filtrate rate(eGFR).Understanding of the test was elicited from a private hospital in Ibadan through the help of an experienced and qualified nurse which also follows same test according to National Kidney Foundation. This knowledge was then used in the developing the simulated and rule-base prediction model using MATLAB software. The paper also follows the 3 major stages of Fuzzy logic. The results of fuzzification of variables, inference, model testing and defuzzification of variables was also presented. This in turn simplifies the complication involved in detecting Chronic Kidney failure/disease using Fuzzy logic based model.

The work proposed in this paper aims to design a robust stabilization of an underactuated quadrotor UAV system in presence of sensor failures. The dynamical model of quadrotor while taking into account various physical phenomena, which can influence the dynamics of a flying structure is presented. Subsequently, a new control strategy based on backstepping approach is developed. Lyapunov based stability analysis shows that the proposed control strategy design keep the stability of the closed loop dynamics of the quadrotor UAV even after the presence of sensor failures. Numerical simulation results are provided to show the good tracking performance of proposed control laws.

This paper presents a fuzzy multi-objective approach for achieving the minimum active power loss and the maximum voltage magnitude in order to improve the efficiency of radial distribution networks with distributed generations. Multi-objective function are considered for load balancing among the feeders,
minimization of the real power loss, deviation of nodes voltage, and branch current constraint violation,while subject to a radial network structure in which all loads must be energized. Originality of the research is that the fuzzy-based multi-objective optimization in reconfiguration of distribution network
including the distributed generation in order to improve the efficiency of the networks. The implementation of the fuzzy multi-objective for distribution reconfiguration on a 70 nodes distribution network with distributed generation is described. The original efficiency of the network is 95.142%. The
simulation results show that efficiency of the network is increased to 96.942% by using fuzzy multiobjective method.

International Journal of Artificial Intelligence and Soft Computing (IJAISC) 
http://flyccs.com/jounals/IJASC/Home.html
Scope
International Journal of Artificial Intelligence and Soft Computing (IJAISC) is a open access journal that publishes articles which contribute new results in all areas of Computer Networks & Communications. The journal focuses on all technical and practical aspects of Computer Networks & data Communications. Authors are solicited to contribute to this journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the Computer Networks & Communications. ________________________________________
Topics of interest include, but are not limited to, the following

• Artificial Intelligence
• AI Algorithms
• Artificial Intelligence tools & Applications
• Automatic Control
• Bioinformatics
• Natural Language Processing
• CAD Design & Testing
• Computer Vision and Speech Understanding
• Data Mining and Machine Learning Tools
• Fuzzy Logic
• Heuristic and AI Planning Strategies and Tools
• Computational Theories of Learning
• Hybrid Intelligent Systems
• Information Retrieval
• Intelligent System Architectures
• Knowledge Representation
• Knowledge-based Systems
• Mechatronics
• Multimedia & Cognitive Informatics
• Neural Networks
• Parallel Processing
• Pattern Recognition
• Robotics
• Semantic Web Techniques and Technologies
• Soft computing theory and applications
• Software & Hardware Architectures • Soft Computing
• Fuzzy Systems
• Neural Networks
• Machine learning
• Probabilistic Reasoning
• Evolutionary Computing
• Pattern recognition
• Hybrid intelligent systems,
• Software agents
• Morphic Computing
• Image processing,
• E-commerce, e-medicine
• Rough Sets
• Symbolic machine learning,
• Wavelet
• Signal or Image Processing
• Vision Recognition
• Biomedical Engineering
• Telecommunications
• Reactive Distributed AI
• Nano & Micro-systems
• Data Visualization
• Pervasive computing and ambient intelligence
• Programming Languages
• Reasoning and Evolution
• Recent Trends and Developments
Paper Submission
Authors are invited to submit papers for this journal through Submission System. Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this Journal. For paper format download the template in this page Manuscript Template
Important Dates
• Submission Deadline : August 31, 2018
• Notification :September 30, 2018
• Final Manuscript Due : October 06, 2018
• Publication Date : Determined by the Editor-in-Chief
TO SUBMIT YOUR PAPER, PLEASE CLICK THE FOLLOWING LINK Submit
Contacts
Here's where you can reach us :JAISCjournal@yahoo.com (or) jaiscjournal@gmail.com

Synergetic Control and synchronization of two different chaotic systems is presented in this paper and simulation results are given illustrating the effectiveness of the robust control technique applied. Similar to the sliding mode approach but without damaging chattering, synergetic control is a robust approach which doesn't require linearization and should be suitable for real-time implementation for not requiring a discontinuous term in its control law.

The increasing demand of World Wide Web raises the need of predicting the user’s web page request. The
most widely used approach to predict the web pages is the pattern discovery process of Web usage mining.
This process involves inevitability of many techniques like Markov model, association rules and clustering.
Fuzzy theory with different techniques has been introduced for the better results. Our focus is on Markov
models. This paper is introducing the vague Rules with Markov models for more accuracy using the vague
set theory.

The International Journal of Applied Control, Electrical and Electronics Engineering (IJACEEE) is an open access peer-reviewed journal that publishes quality articles contributing new results in all areas of Applied Control Systems, Electrical Engineering and Electronics Engineering. The International Journal of Applied Control, Electrical and Electronics Engineering (IJACEEE) is an abstracted and indexed international journal of high quality devoted to the publication of original research papers from Control Systems, Electrical Engineering, Power Systems, Automation, Electronics Engineering, Networks and Communication Systems and their broad range of applications.

To supplement the power shortages, renewable energy sources are a promising source among the existing resources of energy out of which solar PV (photo voltaic) is most popular due to its significant advantages. Controlling the output of solar PV system and harmonics at the load end are key aspects. The main theme of this paper is to control output of solar PV system with MPPT (maximum power point tracking) and reduction of harmonics at the load end.In this paper, commercial solar arrays of 1.2 kW along with P&O (perturb and observe) MPPT to track maximum power has been interfaced with neutral point clamped multilevel inverter.Simulation has been done for different level with the aim to reduce THD (total harmonic distortion) at the load end. Modeling and simulation of solar PV system and DC-DC boost converter has been done using SIMSCAPE library of MATLAB and the output of Boost Converter has been interfaced with neutral point clamped multilevel inverter which has been modeled in SIMULINK library of MATLAB. The advantage of using SIMSCAPE library is that it models the system considering all the physical effects. The output of solar cell has been interfaced with the boost converter. Once the system is modeled in SIMSCAPE it can be easily implemented on hardware for real-time application.

This The integrated circuits (ICs) industry uses a number of technology computer aided design (TCAD) software tools to simulate the manufacturing and the operation of many ICs at different levels. At very low level, the simulation tools are used to simulate the device fabrication and design. These simulation tools are based on solving mathematical equations that describe the physics of dopant diffusion, silicon oxidation, etching, deposition, lithography, implantation, and metallization. The simulation of physical etching solves etching equations to calculate the etching rate. And this rate is used to move the geometry of the device. The simulation of non-physical (geometrical) etching is based on geometrical Boolean operations. In this paper, we are proposing new and advanced geometrical etching algorithms for the process simulation of three dimensional (3D) micro electro mechanical systems (MEMS) and MEMS-tunable vertical cavity semiconductor optical amplifiers (VCSOAs). These algorithms are based on advanced domain decomposition methods, Delaunay meshing algorithms, and surface re-meshing and smoothing techniques. These algorithms are simple, robust, and significantly reduce the overall run time of the process simulation of 3D MEMS and MEMS-tunable laser devices. The description of the proposed etching algorithms will be presented. Numerical simulation results showing the performances of these algorithms will be given and analyzed for realistic 3D MEMS and MEMS-tunable laser devices.