Last edited by Kami
Saturday, May 16, 2020 | History

1 edition of Soft Computing for Control of Non-Linear Dynamical Systems found in the catalog.

Soft Computing for Control of Non-Linear Dynamical Systems

by Oscar Castillo

  • 71 Want to read
  • 10 Currently reading

Published by Physica-Verlag HD, Imprint: Physica in Heidelberg .
Written in English

    Subjects:
  • Mathematical Economics,
  • Mathematical optimization,
  • Computer science,
  • Artificial intelligence

  • About the Edition

    The book describes the application of soft computing techniques to modelling, simulation and control of non-linear dynamical systems. Hybrid intelligence systems, which integrate different techniques and mathematical models, are also presented. The book covers the basics of fuzzy logic, neural networks, evolutionary computation, chaos and fractal theory. It also presents in detail different hybrid architectures for developing intelligent control systems for applications in robotics, reactors, manufacturing, aircraft systems and economics.

    Edition Notes

    Statementby Oscar Castillo, Patricia Melin
    SeriesStudies in Fuzziness and Soft Computing -- 63, Studies in Fuzziness and Soft Computing -- 63
    ContributionsMelin, Patricia
    Classifications
    LC ClassificationsQ334-342, TJ210.2-211.495
    The Physical Object
    Format[electronic resource] /
    Pagination1 online resource (xvi, 221 pp. 112 figs., 13 tabs.)
    Number of Pages221
    ID Numbers
    Open LibraryOL27088037M
    ISBN 103662003678, 3790818321
    ISBN 109783662003671, 9783790818321
    OCLC/WorldCa851368906

      The remainder of the book is devoted to the most promising soft computing techniques, particle swarm optimization techniques, and strategies combining hard and soft controls. In addition, the book: Includes a report on exciting new developments in prosthetic/robotic hand technology, with an emphasis on the fusion of hard and soft control strategies. This Academic Group will contribute to the integration of different Soft Computing (SC) methodologies for the development of hybrid intelligent systems for modeling, simulation and control of non-linear dynamical systems. SC methodologies at the moment include (at least) Neural Networks, Fuzzy Logic, Genetic Algorithms and Chaos Theory.

    This book presents selected fault diagnosis and fault-tolerant control strategies for non-linear systems in a unified framework. In particular, starting from advanced state estimation strategies up to modern soft computing, the discrete-time description of the system is employed Part I of the book presents original research results regarding state estimation and neural networks for robust Author: Marcin Witczak. Modelling, Simulation and Control of Non-Linear Dynamical Systems An Intelligent Approach Using Soft Computing and Fractal Theory Patricia Melin and Oscar Castillo Tijuana Institute of Technology, Tijuana, Mexico Taylor &. Francis Taylor & Francis Group Boca Raton London New York Singapore A CRC title, part of the Taylor & Francis imprint, a Cited by:

    Zadeh describes the principal constituents of soft computing: fuzzy logic, neural networks, and probabilistic reasoning, which in turn subsume belief networks, generic algorithms, parts of learning theory, and chaotic systems. In the second part, Zadeh picks a subset of fuzzy logic, namely the fuzzy graph, as the central topic of by: 1. Modelling, Simulation and Control of Non-linear Dynamical Systems: An Intelligent Approach Using Soft Computing and Fractal Theory - Patricia Melin, Oscar Castillo. Adaptive Inverse Control: A Signal Processing Approach - Bernard Widrow, Eugene Walach.


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Soft Computing for Control of Non-Linear Dynamical Systems by Oscar Castillo Download PDF EPUB FB2

The book describes the application of soft computing techniques to modelling, simulation and control of non-linear dynamical systems. Hybrid intelligence systems, which integrate different techniques and mathematical models, are also by: Modelling, Simulation and Control of Non-linear Dynamical Systems: An Intelligent Approach Using Soft Computing and Fractal Theory (Numerical Insights Book 2) - Kindle edition by Melin, Patricia, Castillo, Oscar.

Download it once and read it on your Kindle device, PC, phones or cturer: CRC Press. We describe in this book, new methods for modelling, simulation, and control of dynamical systems using soft computing techniques and fractal theory.

Soft Computing (SC) consists of several computing paradigms, including fuzzy logic, neural networks, and genetic algorithms, which can be used to produce powerful hybrid intelligent : Oscar Castillo, Patricia Melin.

Soft-Computing-Based Nonlinear Control Systems Design is a critical scholarly publication that examines the practical applications of control theory and its applications in problem solving to fields including economics, environmental management, and financial modelling.

Featuring a wide range of topics, such as fuzzy logic, nature-inspired. Soft Computing for Control of Non-Linear Dynamical Systems. select article Soft Computing for Control of Non-Linear Dynamical Systems. Soft Computing for Control of Non-Linear Dynamical Systems Page Download PDF; select article Special issue on soft computing for control of non-linear dynamical systems systems.

Oscar Castillo, Patricia Melin. Pages Download PDF; select article Model. Soft Computing for Control of Non-Linear Dynamical Systems With Figures and 13 Tables Physica-Verlag A Springer-Verlag Company.

Contents Chapter 1 Introduction to Control of Non-Linear Dynamical Systems 1 Chapter 2 Fuzzy Logic.'. 5 Fuzzy Set Theory 6 Fuzzy Reasoning 11 Fuzzy Inference Systems 15 Adaptive Control of a Cited by: Soft computing consists of fuzzy logic, neural net-works, evolutionary computation, and chaos theory.

Controlling real-world non-linear dynamical systems may require the use of several soft. 4 soft computing: concepts and techniques Fuzzy logic is used in system control and analysis design, because it shortens the time for engineering development and sometimes, in the case of highly Author: Mrutyunjaya Panda.

CNNs' computation-based systems represent new opportunities for improving the soft-computation toolbox. The application of soft computing to complex systems and in particular to chaotic systems with the generation of chaotic dynamics by using CNN is also : Springer-Verlag London.

Patricia Melin, Oscar Castillo These authors use soft computing techniques and fractal theory in this new approach to mathematical modeling, simulation and control of complexion-linear dynamical systems. First, a new fuzzy-fractal approach to automated mathematical modeling of non-linear dynamical systems is presented.

Book Description. These authors use soft computing techniques and fractal theory in this new approach to mathematical modeling, simulation and control of complexion-linear dynamical systems.

First, a new fuzzy-fractal approach to automated mathematical modeling of non-linear dynamical systems. Allied Publishers, - Soft computing - pages. Flow Control Using RM Consolidation for Multicast.

A 8 25GHZ QPSK Modulator for Space Applications. Page - Work at the School of Information Management and Systems at the University of California at Berkeley is 5/5(1).

This book presents a unified view of modelling, simulation, and control of non­ linear dynamical systems using soft computing techniques and fractal theory. Our particular point of view is that modelling, simulation, and control are problems that cannot be considered apart, because they are intrinsically related in real world applications.

Modeling and control of non-linear systems using soft computing techniques. is an attempt to illustrate the utility and effectiveness of soft computing approaches in handling the modeling and control of complex systems. Soft computing research is concerned with the integration of artificial intelligent tools (neural networks, fuzzy Cited by:   These authors use soft computing techniques and fractal theory in this new approach to mathematical modeling, simulation and control of complexion-linear dynamical systems.

First, a new fuzzy-fractal approach to automated mathematical modeling of non-linear dynamical systems is presented. It is illustrated with examples on the PROLOG programming laCited by: Controlling realworld non-linear dynamical systems may require the use of several soft computing techniques to achieve the desired performance in practice.

For this reason, several hybrid intelligent architectures have been developed. The basic idea of these hybrid architectures is to combine the advantages of each of the techniques involved in.

CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): Abstract. We describe in this paper the application of soft computing techniques to controlling non-linear dynamical systems in realworld problems.

Soft computing consists of fuzzy logic, neural networks, evolutionary computation, and chaos theory. Controlling realworld non-linear dynamical systems may require the use. SOFT COMPUTING () MODULE-I (10 HOURS) we have to switch from one control system to another control system where the plant is operating.

The plant is may be operating in a linear zone or non-linear zone; probably an operator can take a very nice intelligent decision about it, but can a. Join Book Program Modelling, Simulation, and Control of Non-Linear Dynamical Systems Written for practicing engineers and advanced students, this book discusses the modeling, simulation, and control of nonlinear dynamic systems using soft computing methods and fractal theory.

Soft computing consists of fuzzy logic, neural networks, evolutionary computation, and chaos theory. Controlling realworld non-linear dynamical systems may require the use of several soft computing techniques to achieve the desired performance in : Patricia Melin and Oscar Castillo.springer, This book describes in a detailed fashion the application of hybrid intelligent systems using soft computing techniques for intelligent control and mobile robotics.

Soft Computing (SC) consists of several intelligent computing paradigms, including fuzzy logic, neural networks, and bio-inspired optimization algorithms, which can be used to produce powerful hybrid intelligent systems.Home Browse by Title Periodicals Applied Soft Computing Vol.

7, No. 3 Modeling and control of non-linear systems using soft computing techniques article Modeling and control of non-linear systems using soft computing techniquesCited by: