Showing papers on "Neuro-fuzzy published in 1996"

Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence

Abstract: Included in Prentice Hall's MATLAB Curriculum Series, this text provides a comprehensive treatment of the methodologies underlying neuro-fuzzy and soft computing. The book places equal emphasis on theoretical aspects of covered methodologies, empirical observations, and verifications of various applications in practice.

Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems

Abstract: Neural Fuzzy Systems provides a comprehensive, up-to-date introduction to the basic theories of fuzzy systems and neural networks, as well as an exploration of how these two fields can be integrated to create Neural-Fuzzy Systems It includes Matlab software, with a Neural Network Toolkit, and a Fuzzy System Toolkit

Fuzzy logic, neural networks, and soft computing

Abstract: The past few years have witnessed a rapid growth of interest in a cluster of modes of modeling and computation which may be described collectively as soft computing. The distinguishing characteristic of soft computing is that its primary aims are to achieve tractability, robustness, low cost, and high MIQ (machine intelligence quotient) through an exploitation of the tolerance for imprecision and uncertainty. Thus, in soft computing what is usually sought is an approximate solution to a precisely formulated problem or, more typically, an approximate solution to an imprecisely formulated problem. A simple case in point is the problem of parking a car. Generally, humans can park a car rather easily because the final position of the car is not specified exactly. If it were specified to within, say, a few millimeters and a fraction of a degree, it would take hours or days of maneuvering and precise measurements of distance and angular position to solve the problem. What this simple example points to is the fact that, in general, high precision carries a high cost. The challenge, then, is to exploit the tolerance for imprecision by devising methods of computation which lead to an acceptable solution at low cost. By its nature, soft computing is much closer to human reasoning than the traditional modes of computation. At this juncture, the major components of soft computing are fuzzy logic (FL), neural network theory (NN), and probabilistic reasoning techniques (PR), including genetic algorithms, chaos theory, and part of learning theory. Increasingly, these techniques are used in combination to achieve significant improvement in performance and adaptability. Among the important application areas for soft computing are control systems, expert systems, data compression techniques, image processing, and decision support systems. It may be argued that it is soft computing, rather than the traditional hard computing, that should be viewed as the foundation for artificial intelligence. In the years ahead, this may well become a widely held position.

Foundations of neural networks, fuzzy systems, and knowledge engineering

Abstract: From the Publisher: "Covering the latest issues and achievements, this well documented, precisely presented text is timely and suitable for graduate and upper undergraduate students in knowledge engineering, intelligent systems, AI, neural networks, fuzzy systems, and related areas. The author's goal is to explain the principles of neural networks and fuzzy systems and to demonstrate how they can be applied to building knowledge-based systems for problem solving. Especially useful are the comparisons between different techniques (AI rule-based methods, fuzzy methods, connectionist methods, hybrid systems) used to solve the same or similar problems." -- Anca Ralescu, Associate Professor of Computer Science, University of Cincinnati Neural networks and fuzzy systems are different approaches to introducing human-like reasoning into expert systems. This text is the first to combine the study of these two subjects, their basics and their use, along with symbolic AI methods to build comprehensive artificial intelligence systems. In a clear and accessible style, Kasabov describes rule- based and connectionist techniques and then their combinations, with fuzzy logic included, showing the application of the different techniques to a set of simple prototype problems, which makes comparisons possible. A particularly strong feature of the text is that it is filled with applications in engineering, business, and finance. AI problems that cover most of the application-oriented research in the field (pattern recognition, speech and image processing, classification, planning, optimization, prediction, control, decision making, and game simulations) are discussed and illustrated with concrete examples. Intended both as a text for advanced undergraduate and postgraduate students as well as a reference for researchers in the field of knowledge engineering, Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering has chapters structured for various levels of teaching and includes original work by the author along with the classic material. Data sets for the examples in the book as well as an integrated software environment that can be used to solve the problems and do the exercises at the end of each chapter are available free through anonymous ftp.

On fuzzy algorithms

Abstract: Unlike most papers in Information and Control, our note contains no theorems and no proofs. Essentially, its purpose is to introduce a basic concept which, though fuzzy rather than precise in nature, may eventually prove to be of use in a wide variety of problems relating to information processing, control, pattern recognition, system identification, artificial intelligence and, more generally, decision processes involving incomplete or uncertain data. The concept in question will be called a fuzzy algorithm because it may be viewed as a generalization, through the process of fuzzification, of the conventional (nonfuzzy) conception of an algorithm. More specifically, unlike a nonfuzzy deterministic or nondeterministic algorithm (Floyd, 1967), a fuzzy algorithm may contain fuzzy statements, that is, statements containing names of fuzzy sets (Zadeh, 1965), by which we mean classes in which there may be grades of membership intermediate between full membership and nonmembership. To illustrate, fuzzy algorithms may contain fuzzy instructions such as:

On fuzzy mapping and control

Abstract: A fuzzy mapping from X to Y is a fuzzy set on X × Y. The concept is extended to fuzzy mappings of fuzzy sets on X to Y, fuzzy function and its inverse, fuzzy parametric functions, fuzzy observation, and control. Set theoretical relations are obtained for fuzzy mappings, fuzzy functions, and fuzzy parametric functions. It is shown that under certain conditions a precise control goal can be attained with fuzzy observation and control as long as the observations become sufficiently precise when the goal is approached.

Soft computing and fuzzy logic

Abstract: Discusses soft computing, a collection of methodologies that aim to exploit the tolerance for imprecision and uncertainty to achieve tractability, robustness, and low solution cost. Its principal constituents are fuzzy logic, neurocomputing, and probabilistic reasoning. Soft computing is likely to play an increasingly important role in many application areas, including software engineering. The role model for soft computing is the human mind. >

Computational intelligence PC tools

Abstract: Computational intelligence is an emerging field in computer science which combines fuzzy logic, neural networks, and genetic algorithms for a flexible yet powerful approach to scientific computing. Because computational intelligence combines three interrelated, mathematically-based tools, it has a wide variety of applications, from engineering and process control to experts systems. This book takes a hands-on, desktop-applications approach to the topic, featuring examples of specific real-world implementations and detailed case studies, with all pertinent code and software included on a floppy disk packaged with the book. Features: * Concise introduction to the concepts of fuzzy logic, neural networks, and genetic algorithms, and how they relate to one another within the context of computational intelligence. * Computational intellignece applications, including self-organizing feature maps, fuzzy calculator, evolutionary programming, and fuzzy neural networks. * Detailed case studies from engineering (F-16 flight system), systems control (mass transit scheduling), and medicine (appendicitis diagnosis). * Windows floppy disk with both source code and executable, self-contained programs for desktop implementation of all of the book's applications.

Fuzzy Algorithms: With Applications to Image Processing and Pattern Recognition

Abstract: Introduction: Fuzzy sets probability and fuzziness fuzzy models Membership functions: heuristic selections clustering approaches adjustment and toning applications concluding remarks Fuzzy clustering: clustering and fuzzy partition fuzzy c-means algorithm fuzzy cohonen clustering networks cluster validity and optimal fuzzy clustering applications concluding remarks Fuzzy rules and defuzzification: rules based on experience learning from examples decision tree approach neural network approach minimization of fuzzy rules defuzzification and optimization applications concluding remarks Fuzzy classifiers: fuzzy nearest neighbour classifier fuzzy multilayer perceptron fuzy decision trees fuzzy string matching applications concluding remarks Combined clasifications: introduction voting schemes maximum poteriori probability Dempster-Shafer evidence theory trained perceptron neural networks applications concluding remarks

An Introduction to Fuzzy Logic for Practical Applications

Abstract: Fuzzy logic has become an important tool for a number of different applications ranging from the control of engineering systems to artificial intelligence. In this concise introduction, the author presents a succinct guide to the basic ideas of fuzzy logic, fuzzy sets, fuzzy relations, and fuzzy reasoning, and shows how they may be applied. The book culminates in a chapter which describes fuzzy logic control: the design of intelligent control systems using fuzzy if-then rules which make use of human knowledge and experience to behave in a manner similar to a human controller. Throughout, the level of mathematical knowledge required is kept basic and the concepts are illustrated with numerous diagrams to aid in comprehension. As a result, all those curious to know more about fuzzy concepts and their real-world application will find this a good place to start.

Stable adaptive fuzzy controllers with application to inverted pendulum tracking

Abstract: An adaptive fuzzy controller is constructed from a set of fuzzy IF-THEN rules whose parameters are adjusted on-line according to some adaptation law for the purpose of controlling the plant to track a given-trajectory. In this paper, two adaptive fuzzy controllers are designed based on the Lyapunov synthesis approach. We require that the final closed-loop system must be globally stable in the sense that all signals involved (states, controls, parameters, etc.) must be uniformly bounded. Roughly speaking, the adaptive fuzzy controllers are designed through the following steps: first, construct an initial controller based on linguistic descriptions (in the form of fuzzy IF-THEN rules) about the unknown plant from human experts; then, develop an adaptation law to adjust the parameters of the fuzzy controller on-line. We prove, for both adaptive fuzzy controllers, that: (1) all signals in the closed-loop systems are uniformly bounded; and (2) the tracking errors converge to zero under mild conditions. We provide the specific formulas of the bounds so that controller designers can determine the bounds based on their requirements. Finally, the adaptive fuzzy controllers are used to control the inverted pendulum to track a given trajectory, and the simulation results show that: (1) the adaptive fuzzy controllers can perform successful tracking without using any linguistic information; and (2) after incorporating some linguistic fuzzy rules into the controllers, the adaptation speed becomes faster and the tracking error becomes smaller.

Fuzzy logic and neural network handbook

Abstract: Fuzzy logic principles and algorithms design guidelines for fuzzy logic systems enhanced training algorithms maximum likelihood training temporal difference learning NADINE hybrid learning systems explanation and reasoning within connectivist systems extracting fuzzy rules fuzzy logic framework for managing aquatic ecosystems neural nets for seafloor classification stock market prediction face recognition acoustic transient analysis remotely-sensed imagery power systems telecommuncations handprinted character recognition financial analysis non-destructive evaluation of materials segmentation of medical images sonar signal processing radar signal processing automatic speech recognition recurrent neural networks CMAC neural networks real-time image segmentation fuzzy max-min neural network system fuzzy controller integrated neural computing architecture general asymmetric neural networks.

Combining fuzzy information from multiple systems (extended abstract)

Abstract: Fuzzy Information from Multiple Systems

A note on prototype theory and fuzzy sets

Abstract: In a recent paper, Osherson and Smith (198 1 i present an insig of some of the contending approaches to prototype theory and arrive a.l: the conclusiii3n that the theory of fuzzy sets does not provide a satisfactory solution to the problems that stand in the v~ay of constructing an adequate theory of prototypes. In what follows, the issues raised by Osherson and Smith are commented upon and an alternative definition of the concept of a prototype is proposed. In contrast to some of the conventional definitions, the proposed definition does not associate with a given set A a ur)lique prototypical element of A. Rather, the prototypes of A constitute a fuzzy set, PI’(A), whose elements, in general, are not elements of A. Viewed in this perspective, the concept of a prototype is a fuzzy concept, and the theory of fuzzy sets provides an appropriate framework for its formulation and applications. The first issue raised by Osherson and Smith relates to the observation that, in the case of conjunctive concepts, e.g., a striped apple, the rules of combination of fuzzy sets lead to a contradiction when an object is more prototypical of a conjunction than of its constituents. The same point was raised earlier by Paul Kay (1973, who observed that, in some cases, the grade of membership of an object, u, in the intersection of two fuzzy sets A and I3 may be greater than its grade of membership in A (or B). In explaining this phenomenon (Zadeh, 1978), it was pointed out that when (a) the intersection of A and B is a subnormal fuzzy set (i.e., a fuzzy set whose maximal grade of membership is less than unity); and (b) we focus our attention on A n B by giving it a label, say C, we are, in effect, tacitly normalizing C by relativizing the grades of membership in C with respect to the maximal grade of membership in A n B. By so doing, we are generating a normalized fuzzy set Norm (A n B) which is not a subset of A and B. Consequently, an object, u, may ha-‘*. :, higher grade of membership in Norm (A n B) than in A or B.

Reservoir Operating Rules with Fuzzy Programming

Abstract: Relatively little of the research on reservoir operating procedures has found its way into actual practice. One reason is that operators are uncomfortable with complex optimization models and reluctant to use procedures that they do not fully understand. Fuzzy logic seems to offer a way to improve on existing operating practices, which is relatively easy to explain and understand. The main concepts in fuzzy logic and a procedure for applying them are explained. These are applied to finding operating procedures for a single-purpose hydroelectric project, where both the inflows and the selling price for energy can vary. Operation of the system is simulated using both fuzzy logic programming and fixed rules. The results are compared with those obtained by deterministic dynamic programming with hindsight. The use of fuzzy logic with flow forecasts is also investigated. The conclusion is that the fuzzy logic approach is promising, but it suffers from the “curse of dimensionality.” It can be a useful supplement...

Fuzzy function approximation with ellipsoidal rules

Abstract: A fuzzy rule can have the shape of an ellipsoid in the input-output state spare of a system. Then an additive fuzzy system approximates a function by covering its graph with ellipsoidal rule patches. It averages rule patches that overlap. The best fuzzy rules cover the extrema or bumps in the function. Neural or statistical clustering systems can approximate the unknown fuzzy rules from training data. Neural systems can then both tune these rules and add rules to improve the function approximation. We use a hybrid neural system that combines unsupervised and supervised learning to find and tune the rules in the form of ellipsoids. Unsupervised competitive learning finds the first-order and second-order statistics of clusters in the training data. The covariance matrix of each cluster gives an ellipsoid centered at the vector or centroid of the data cluster. The supervised neural system learns with gradient descent. It locally minimizes the mean-squared error of the fuzzy function approximation. In the hybrid system unsupervised learning initializes the gradient descent. The hybrid system tends to give a more accurate function approximation than does the lone unsupervised or supervised system. We found a closed-form model for the optimal rules when only the centroids of the ellipsoids change. We used numerical techniques to find the optimal rules in the general case.

Large-Scale Systems: Modeling, Control and Fuzzy Logic

Abstract: Preface. 1. Introduction to Large-Scale Systems. Historical Background. Hierarchical Structures. Decentralized Control. Artificial Intelligence. Neural Networks. Fuzzy Logic. Computer-Aided Approach. Scope. Problems. 2. Large-Scale Systems Modeling. Introduction. Aggregation Methods. General Aggregation. Modal Aggregation. Balanced Aggregation. Perturbation Methods. Weakly Coupled Models. Strongly Coupled Models. Modeling via System Identification. Problem Definition. System ID Toolbox. Modeling via Fuzzy Logic. Problems. 3. Structural Properties of Large Scale Systems. Introduction. Lyapunov Stability Methods. Definitions and Problem Statement. Stability Criteria. Connective Stability. Input-Output Stability Methods. Problem Development and Statement. IO Stability Criterion. Controllability and Observability of Composite Systems via Connectivity Approach. Preliminary Definitions. Controllability and Observability Conditions. Structural Controllability and Observability. Structure and Rank of a Matrix. Conditions for Structural Controllability. Structural Controllability and Observability via System Connectability. Computer-Aided Structural Analysis. Standard State-Space Forms. CAD Examples. Discussion and Conclusions. Discussion of the Stability of Large-Scale Systems. Discussion of the Controllability and Observability of Large-Scale Systems. Problems. 4. Hierarchical Control of Large-Scale Systems. Introduction. Coordination of Hierarchical Structures. Model Coordination Method. Goal Coordination Method. Hierarchical Control of Linear Systems. Linear System Two-level Coordination. Interaction Prediction Method. Goal Coordination and Singularities. Closed-Loop Hierarchical Control of Continuous-Time Systems. Series Expansion Approach of Hierarchical Control. Problem Formulation. Performance Index Approximation. Optimal Control. Coorinator Problem. Computer-Aided Hierarchical Control Design Examples. Problems. 5. Decentralized Control of Large-Scale Systems. Introduction. Decentralized Stabilization. Fixed Polynomials and Fixed Modes. Stabilization via Dynamic Compensation. Stabilization via Multilevel Control. Exponential Stabilization. Decentralized Adaptive Control. Decentralized Adaptation. Decentralized Regulation Systems. Decentralized Tracking Systems. Liquid-Metal Cooled Reactor. Application of Model Reference Adaptive Control. Discussion and Conclusions. Problems. 6. Near-Optimum Design of Large-Scale Systems. Introduction. Near-Optimum Control of Linear Time-Invariant Systems. Aggregation Methods. Perturbation Methods. Decentralized Control via Unconstrained Minimization. Near-Optimum Control of Large-Scale Nonlinear Systems. Near-Optimum Control via Sensitivity Methods. Hierarchical Control via Interaction Prediction. Bounds on Near-Optimum Cost Functional. Near-Optimality Due to Aggregation. Near-Optimality Due to Perturbation. Near-Optimality in Hierarchical Control. Near-Optimality in Nonlinear Systems. Computer-Aided Design. Problems. 7. Fuzzy Control Systems-Structures and Stability. Introduction. Fuzzy Control Structures. Basic Definitions and Architectures. Fuzzification. Inference Engine. Defuzzification Methods. The Inverted Pendulum Problem. Overshoot-Suppressing Fuzzy Controllers. Analysis of Fuzzy Control System. Stability of Fuzzy Control Systems. Introduction. Fuzzy Control Systems' Stability Classes. Lyapunov Stability of Fuzzy Control Systems. Fuzzy System Stability via Interval Matrix. Method. Problems. 8. Fuzzy Control Systems-Adaptation and Hierarchy. Introduction. Adaptive Fuzzy Control Systems. Adaptation by Parameter Estimation. Adaptive Fuzzy Multiterm Controllers. Indirect Adaptive Fuzzy Control. Large-Scale Fuzzy Control Systems. Hierarchical Fuzzy Control. Rule-Base Reduction. Hybrid Control Systems. Problems. Appendix A. Brief Review of Fuzzy Set Theory. Introduction. Fuzzy Sets versus Crisp Sets. The Shape of Fuzzy Sets. Fuzzy Sets Operations. Fuzzy Logic and Approximate Reasoning. Problems. Apprendix B. The Fuzzy Logic Development Kit. Introduction. Description of the FULDEK Program. EDITOR Option. The RUN Option. Post-Processing Feature of FULDEK. A Real-Time Laser Beam Fuzzy Controller. New Options in Version 4.0 of the FULDEK Program. Conclusion. References. Index.

Fuzzy systems with defuzzification are universal approximators

Abstract: In this paper, we consider a fundamental theoretical question: Is it always possible to design a fuzzy system capable of approximating any real continuous function on a compact set with arbitrary accuracy? Moreover, we research whether the answer to the above question is positive when we restrict to a fixed (but arbitrary) type of fuzzy reasoning and to a subclass of fuzzy relations. This result can be viewed as an existence theorem of an optimal fuzzy system for a wide variety of problems.

Fuzzy Logic and NeuroFuzzy Applications in Business and Finance

Abstract: 1. Fuzzy Logic Primer. The Fuzzy Logic Benefit. Sample Applications-Types of Uncertainty-A "Fuzzy" Set. A Case Study on Fuzzy Logic Inference. Financial Liquidity Evaluation Example-Conventional Decision Support Techniques-Linguistic Decision making. The Fuzzy Logic Algorithm. Fuzzification Using Linguistic Variables-Fuzzy Logic Inference Using If-Then Rules-Defuzzification Using Linguistic Variables. More Fuzzy Logic Theory. 2. Getting Started with fuzzyTECH for Business. Installation Guide. License Agreement-Installing fuzzyTECH and the Samples-Conventions-First Steps. Basic System Design Methodology. Using the Fuzzy Design Wizard-Creating a Rule Base- Interactive Debugging-File Debugging and Analyzers. Extending the System. Adding New Components-Interactive Debugging of Complex Projects-Advanced Features of fuzzyTECH-fuzzyTECH's Revision Control System-Creating Stand-Alone Solutions. 3. Getting Started with NeuroFuzzy Design. NeuroFuzzy Technology. Adaptive Systems and Neural Networks-Combining Neural and Fuzzy-NeuroFuzzy vs. Other Adaptive Technologies. Training Examples. Using the fuzzyTECH NeuroFuzzy Module-Training the Creditworthiness Evaluation-NeuroFuzzy Training in Data Analysis. Data Clustering. Clustering Techniques-Clustering with fuzzyTECH-Fuzzy Clustering of NeuroFuzzy Training Data. 4. Integration of Fuzzy Logic with Standard Software. Using DDE and DLL links with fuzzyTECH. Integration Link Overview-DDE Link-Programming fuzzyTECH Using the DLL Link. Integration of fuzzyTECH with MS-Excel. Installing the fuzzyTECH Assistant-Creating a Fuzzy Logic Spreadsheet-Stocks Analysis Case Study. Integration of fuzzyTECH with VisualBasic. Single Call Remote Interface Using VisualBasic-Standard Call Remote Interface Using VisualBasic-A Case Study Using VisualBasic. Integration of fuzzyTECH with MS-Access. Integration of Fuzzy Logic Functions-The FT Investment Bank Case Study-FT Investment Bank's MS-Access Database- AccessBasic Integration. 5. Case Studies of Fuzzy Logic Applications. Fuzzy Logic in Finance Applications. Fuzzy Scoring for Mortgage Applicants-Creditworthiness Assessment-Fraud Detection-Other Finance Applications. Fuzzy Logic in Business Applications. Supplier Evaluation for Sample Testing-Customer Targeting-Sequencing and Scheduling-Optimizing Research and Development Projects-Knowledge-Based Prognosis. Fuzzy Logic in Data Analysis Applications. Fuzzy Data Analysis in Cosmetics-Other Fuzzy Data Analysis Applications. 6. Advanced Fuzzy Logic Design Techniques. Linguistic Variables and Their Membership Functions. Design Methodology of Linguistic Variables-Linear Standard Membership Functions-Membership Function Shapes. Fuzzy Interfaces. Defining Fuzzy Interfaces-Building Explanatory Components. Fuzzy Inference Methods. Premise Aggregation with Fuzzy Logic Operators-Result Aggregation-Matrix Rule Representation. Defuzzification Methods. Best Compromise vs. Most Plausible Result-Comparison of Defuzzification Methods-Information Reduction by Defuzzification. 7. Bibliography. 8. Index.

Approximation accuracy analysis of fuzzy systems as function approximators

Abstract: This paper establishes the approximation error bounds for various classes of fuzzy systems (i.e., fuzzy systems generated by different inferential and defuzzification methods). Based on these bounds, the approximation accuracy of various classes of fuzzy systems is analyzed and compared. It is seen that the class of fuzzy systems generated by the product inference and the center-average defuzzifier has better approximation accuracy and properties than the class of fuzzy systems generated by the min inference and the center-average defuzzifier, and the class of fuzzy systems defuzzified by the MoM defuzzifier. In addition, it is proved that fuzzy systems can represent any linear and multilinear function and explicit expressions of fuzzy systems generated by the MoM defuzzified method are given.

Stability analysis and design for a class of continuous-time fuzzy control systems

Abstract: This paper presents a design method for a class of fuzzy control systems. The class of fuzzy systems considered can be represented by the Takagi-Sugeno fuzzy model which is a type of dynamic fuzzy model. A constructive algorithm is developed to obtain the stabilizing feedback control law for the system. The main contribution of this paper is the development of an equivalent principle; that is, the design of a fuzzy control system is equivalent to the design of a set of linear time-invariant ‘extreme’ systems. Thus any design method in linear control system theory can be used to design a fuzzy control system. An example is given to illustrate the application of the method.