A most prominent phenomenon of natural lan-guages is variability-stating the same meaning in various ways. Robust language processing applica-tions-like Information Retrieval (IR), Question Answering (QA), Information Extraction (IE),... more
A most prominent phenomenon of natural lan-guages is variability-stating the same meaning in various ways. Robust language processing applica-tions-like Information Retrieval (IR), Question Answering (QA), Information Extraction (IE), text summarization and machine translation-must recognize the different forms in which their inputs and requested outputs might be expressed. Today, inferences about language variability are often per-formed by practical systems at a
This paper describes the design and implementation of robust nonlinear sliding mode control strategies for robot manipulators whose dynamic or kinematic models are uncertain. Therefore a fuzzy sliding mode tracking controller for robot... more
This paper describes the design and implementation of robust nonlinear sliding mode control strategies for robot manipulators whose dynamic or kinematic models are uncertain. Therefore a fuzzy sliding mode tracking controller for robot manipulators with uncertainty in the kinematic and dynamic models is design and analyzes. The controller is developed based on the unit quaternion representation so that singularities associated with the otherwise commonly used three parameter representations are avoided. Simulation results for a planar application of the continuum or hyper-redundant robot manipulator (CRM) are provided to illustrate the performance of the developed adaptive controller. These manipulators do not have rigid joints, hence, they are difficult to model and this leads to significant challenges in developing high-performance control algorithms. In this research, a joint level controller for continuum robots is described which utilizes a fuzzy methodology component to compensate for dynamic uncertainties.
We report on a fuzzy logic-based language understanding system applied to speech recognition. This system acquires conceptual knowledge from corpus data and organizes such knowledge into fuzzy logic inference rules. The system parses... more
We report on a fuzzy logic-based language understanding system applied to speech recognition. This system acquires conceptual knowledge from corpus data and organizes such knowledge into fuzzy logic inference rules. The system parses speech recognition results into conceptual structures in a robust manner, and thus is able to tolerate noise caused by speech recognition errors. We discuss the fuzzy inference rule learning method and explain its organization. Experimental results that demonstrate the ability of the system to deal with complex speech input instances are reported
The focus of this research is on the development, modeling and high precision robust control of an electro-mechanical continuum robot manipulator that serves as a sensing and motion system for hybrid testing. In this research parallel... more
The focus of this research is on the development, modeling and high precision robust control of an electro-mechanical continuum robot manipulator that serves as a sensing and motion system for hybrid testing. In this research parallel fuzzy logic theory is used to compensate the system dynamic uncertainty controller based on sliding mode theory. This design resulted in strongly non-linear and coupled dynamics as well as an inertial moving platform that attracted model-based control strategies. A novel non-linear control technique based on sliding mode Lyapunov based was selected to meet the multiple simultaneous specification control of nonlinear, uncertain and asymptotic tracking. Sliding mode controller (SMC) is a significant nonlinear controller under condition of partly uncertain dynamic parameters of system. This controller is used to control of highly nonlinear systems especially for continuum robot manipulator, because this controller is robust and stable in presence of partly uncertainties. Sliding mode controller was used to achieve a stable tracking, while the parallel fuzzy-logic optimization added intelligence to the control system through an automatic tuning of the sliding mode methodology uncertainties. Simulation results demonstrated the validity of the Mamdani parallel fuzzy-optimization control with asymptotic and stable tracking at different position inputs. This compensation demonstrated a well synchronized control signal at different excitation conditions.
In this paper, a PD-serial fuzzy based robust nonlinear estimator for a robot manipulator is proposed by using robust factorization approach. That is, considering the uncertainties of dynamic model consisting of measurement error and... more
In this paper, a PD-serial fuzzy based robust nonlinear estimator for a robot manipulator is proposed by using robust factorization approach. That is, considering the uncertainties of dynamic model consisting of measurement error and disturbances, a PD with fuzzy estimator variable structure nonlinear feedback control scheme is designed to reduce effect of uncertainties. This research aims to design a new methodology to fix the position in robot manipulator. PD method is a linear methodology which can be used for highly nonlinear system’s (e.g., robot manipulator). To estimate this method, new serial fuzzy variable structure method (PD.FVSM) is used. This estimator can estimate the parameters to have the best performance.
Past research (Kruger, Wirtz, Van Boven, & Altermatt, 2004) proposed that people use the effort of the producer as a heuristic for the quality of the product. In contrast, two experiments show that consumers'... more
Past research (Kruger, Wirtz, Van Boven, & Altermatt, 2004) proposed that people use the effort of the producer as a heuristic for the quality of the product. In contrast, two experiments show that consumers' inferences from effort information are highly malleable. Participants were either explicitly exposed to one of two applicable naive theories (“good-art-takes-effort” vs. “good-art-takes-talent”) or the order of
We propose a new approach to fault detection and diagnosis in third-generation (3G) cellular networks using competitive neural algorithms. For density estimation purposes, a given neural model is trained with data vectors representing... more
We propose a new approach to fault detection and diagnosis in third-generation (3G) cellular networks using competitive neural algorithms. For density estimation purposes, a given neural model is trained with data vectors representing normal behavior of a CDMA2000 cellular system. After training, a normality profile is built from the sample distribution of the quantization errors of the training vectors. Then, we find empirical confidence intervals for testing hypotheses of normal/abnormal functioning of the cellular network. The trained network is also used to generate inference rules that identify the causes of the faults. We compare the performance of four neural algorithms and the results suggest that the proposed approaches outperform current methods.
Design a nonlinear controller for second order nonlinear uncertain dynamical systems is one of the most important challenging works. This paper focuses on the design, implementation and analysis of a chattering free sliding mode... more
Design a nonlinear controller for second order nonlinear uncertain dynamical systems is one of
the most important challenging works. This paper focuses on the design, implementation and
analysis of a chattering free sliding mode controller for highly nonlinear dynamic PUMA robot
manipulator and compare to computed torque controller, in presence of uncertainties. In order to
provide high performance nonlinear methodology, sliding mode controller and computed torque
controller are selected. Pure sliding mode controller and computed torque controller can be used
to control of partly known nonlinear dynamic parameters of robot manipulator. Conversely, pure
sliding mode controller is used in many applications; it has an important drawback namely;
chattering phenomenon which it can causes some problems such as saturation and heat the
mechanical parts of robot manipulators or drivers. In order to reduce the chattering this research
is used the linear saturation function boundary layer method instead of switching function method
in pure sliding mode controller. These simulation models are developed as a part of a software
laboratory to support and enhance graduate/undergraduate robotics courses, nonlinear control
courses and MATLAB/SIMULINK courses at research and development company (SSP Co.)
research center, Shiraz, Iran.
Abstract. Unique Fixpoint Induction, UFI, is a chief inference rule to prove the equivalence of recursive processes in CCS [7]. It plays a major role in the equational approach to verification. This approach is of spe-cial interest as it... more
Abstract. Unique Fixpoint Induction, UFI, is a chief inference rule to prove the equivalence of recursive processes in CCS [7]. It plays a major role in the equational approach to verification. This approach is of spe-cial interest as it offers theoretical advantages in the ...
Refer to this paper, design lookup table changed adaptive fuzzy sliding mode controller with minimum rule base and good response in presence of structure and unstructured uncertainty is presented. However sliding mode controller is one... more
Refer to this paper, design lookup table changed adaptive fuzzy sliding mode controller with
minimum rule base and good response in presence of structure and unstructured uncertainty is
presented. However sliding mode controller is one of the robust nonlinear controllers but when
this controller is applied to robot manipulator with highly nonlinear and uncertain dynamic function;
caused to be challenged in control. Sliding mode controller in presence of uncertainty has two
most important drawbacks; chattering and nonlinear equivalent part which proposed method is
solved these challenges with look up table change methodology. This method is based on self
tuning methodology therefore artificial intelligence (e.g., fuzzy logic method) is played important
role to design proposed method. This controller has acceptable performance in presence of
uncertainty (e.g., overshoot=0%, rise time=0.8 s, steady state error = 1e-9 and RMS
error=0.00017).
Refer to the research, review of sliding mode controller is introduced and application to robot manipulator has proposed in order to design high performance nonlinear controller in the presence of uncertainties. Regarding to the... more
Refer to the research, review of sliding mode controller is introduced and application to robot manipulator
has proposed in order to design high performance nonlinear controller in the presence of uncertainties.
Regarding to the positive points in sliding mode controller, fuzzy logic controller and adaptive method, the
output in most of research have improved. Each method by adding to the previous algorithm has covered
negative points. Obviously robot manipulator is nonlinear, and a number of parameters are uncertain, this
research focuses on comparison between sliding mode algorithm which analyzed by many researcher.
Sliding mode controller (SMC) is one of the nonlinear robust controllers which it can be used in uncertainty
nonlinear dynamic systems. This nonlinear controller has two challenges namely nonlinear dynamic
equivalent part and chattering phenomenon. A review of sliding mode controller for robot manipulator will
be investigated in this research
Reading comprehension is an essential component of lifelong learning. Individuals who experience difficulties reading and understanding information presented to them tend to suffer from problems in school, in the workplace, and in their... more
Reading comprehension is an essential component of lifelong learning. Individuals who experience difficulties reading and understanding information presented to them tend to suffer from problems in school, in the workplace, and in their communities (National Center for Education Statistics, 2002; National Reading Panel, 2000). Not only is formal education difficult for these individuals, but the opportunities to reflect, share ideas, and reason effectively are hindered in all aspects of their lives. Thus, it is essential that we understand ...
This paper examines single input single output (SISO) chattering free variable structure control (VSC) which controller coefficient is on-line tuned by fuzzy backstepping algorithm. VSC methodology is selected as a framework to construct... more
This paper examines single input single output (SISO) chattering free variable structure control (VSC) which controller coefficient is on-line tuned by fuzzy backstepping algorithm. VSC methodology is selected as a framework to construct the control law and address the stability and robustness of the close loop system based on Lyapunove formulation. The main goal is to guarantee acceptable trajectories tracking between the robot arm actual output and the desired input. The proposed approach effectively combines the design technique from variable structure controller is based on Lyapunov and fuzzy estimator to estimate the nonlinearity of undefined system dynamic in backstepping controller. The input represents the function between variable structure function, error and the rate of error. The outputs represent fuel ratio, respectively. The fuzzy backstepping methodology is on-line tune the variable structure function based on adaptive methodology. The performance of the SISO VSC which controller coefficient is on-line tuned by fuzzy backstepping algorithm (FBSAVSC) is validated through comparison with VSC and proposed method. Simulation results signify good performance of trajectory in presence of uncertainty and external disturbance.
To know finished priority scale of case in Laboratory of Forensic, in this thesis we developed an expert system with forward and backward chaining methods rule based, used some criteria of case. Priority scale of case is done to sort data... more
To know finished priority scale of case in Laboratory of Forensic, in this thesis we developed an expert system with forward and backward chaining methods rule based, used some criteria of case. Priority scale of case is done to sort data and determine the stage of case, which will be used, for examination. This process needs an evaluation in a number on aspect in accordance with the criteria and characteristics of the case. The capabilities of this system accommodates input data cases which user needs, priority scale and stages according to the standard operating procedure in Indonesian National Police especially in Laboratory of Forensic branch in Semarang and gives the visual picture like groove determining of the priority scale also the best solution from the several alternatives used forward and backward chaining methods rule based. The result showed a good and ideal decision for solved case by quickly recommendation and main priority of case with the result of compatibility level was 100%.
In this paper we formulate the problem of inference under incomplete information in very general terms. This includes modelling the process responsible for the incompleteness, which we call the incompleteness process. We allow the... more
In this paper we formulate the problem of inference under incomplete information in very general terms. This includes modelling the process responsible for the incompleteness, which we call the incompleteness process. We allow the process' behaviour to be partly unknown. Then we use Walley's theory of coherent lower previsions, a generalisation of the Bayesian theory to imprecision, to derive the
We describe a rule-based programming system where rules specify nondeterministic computations. The system is called FunLog and has constructs for defining elementary rules, and to build up complex rules from simpler ones via operations... more
We describe a rule-based programming system where rules specify nondeterministic computations. The system is called FunLog and has constructs for defining elementary rules, and to build up complex rules from simpler ones via operations akin to the standard operations from abstract rewriting. The system has been implemented in Mathematica and is, in particular, useful to program procedures which can be encoded as sequences of rule applications which follow a certain reduction strategy. In particular, the procedures for unification with sequence variables in free, flat, and restricted flat theories can be specified via a set of inference rules which should be applied in accordance with a certain strategy. We illustrate how these unification procedures can be expressed in our framework.
This paper provides a survey of various data mining techniques for advanced database applications. These include association rule generation, clustering and classification. With the recent increase in large online repositories of... more
This paper provides a survey of various data mining techniques for advanced database applications. These include association rule generation, clustering and classification. With the recent increase in large online repositories of information, such techniques have great importance. The focus is on high dimensional data spaces with large volumes of data. The paper discusses past research on the topic and also studies the corresponding algorithms and applications.
Isabelle, which is available from http://isabelle. in. tum. de, is a generic framework for interactive theorem proving. The Isabelle/Pure meta-logic allows the formalization of the syntax and inference rules of a broad range of... more
Isabelle, which is available from http://isabelle. in. tum. de, is a generic framework for interactive theorem proving. The Isabelle/Pure meta-logic allows the formalization of the syntax and inference rules of a broad range of object-logics following the general idea of natural deduction [32, 33]. The logical core is implemented according to the well-known “LCF approach” of secure inferences as abstract datatype constructors in ML [16]; explicit proof terms are also available [8]. Isabelle/Isar provides sophisticated extra-logical ...
An important field of probability logic is the investigation of inference rules that propagate point probabilities or, more generally, interval probabilities from premises to conclusions. Conditional probability logic (CPL) interprets the... more
An important field of probability logic is the investigation of inference rules that propagate point probabilities or, more generally, interval probabilities from premises to conclusions. Conditional probability logic (CPL) interprets the common sense expressions of the form “if..., then...” by conditional probabilities and not by the probability of the material implication. An inference rule is probabilistically informative if the coherent probability interval of its conclusion is not necessarily equal to the unit interval $[0, 1] $. Not all logically valid ...
In the feature subset selection problem, a learning algorithm is faced with the problem of selecting a relevant subset of features upon which to focus its attention, while ignoring the rest. To achieve the best possible performance with a... more
In the feature subset selection problem, a learning algorithm is faced with the problem of selecting a relevant subset of features upon which to focus its attention, while ignoring the rest. To achieve the best possible performance with a particular learning algorithm on a particular training set, a feature subset selection method should consider how the algorithm and the training set interact. We explore the relation between optimal feature subset selection and relevance. Our wrapper method searches for an optimal feature subset tailored to a particular algorithm and a domain. We study the strengths and weaknesses of the wrapper approach and show a series of improved designs. We compare the wrapper approach to induction without feature subset selection and to Relief, a filter approach to feature subset selection. Significant improvement in accuracy is achieved for some datasets for the two families of induction algorithms used: decision trees and Naive-Bayes.
