Intelligent Control: Aspects of Fuzzy Logic and Neural Networks


Harris, C.J., Moore, C.G. and Brown, M. (1993) Intelligent Control: Aspects of Fuzzy Logic and Neural Networks, World Scientific Press (Robotics and Automated Systems, 6).

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Index: 1. An Introduction to Intelligent Control 1.1 Preliminaries 1.2 Intelligent Control Requirements and Architectures 1.3 Approaches to Intelligent Control 1.4 Knowledge Based Systems 1.5 Fuzzy Logic 1.6 Fuzzy Logic in Control 1.7 Neurocontrollers 1.8 Higher Level Intelligent Controllers 1.9 Bibliographical Notes 2. Introductory Fuzzy Logic 2.1 Fuzzy Sets and Logic 2.2 Fuzzy Inference and Composition 2.3 Defuzzification 3. Fuzzy Logic Controller Structure and Design 3.1 Introduction 3.2 Applications of Fuzzy Set Theory 3.3 Fuzzy Logic Controller Structural Issues 3.4 Design Requirements of Fuzzy Logic Controllers 4. The Static Fuzzy Logic Controller 4.1 Introduction 4.2 Controller Design by Verbalisation or Expert Interrogation 4.3 The Fuzzy PID Controller 4.4 Parametrically Determined Fuzzy PID Controllers 4.5 Linguistic Rule Inversion Fuzzy Logic Controllers 4.6 Cluster Based Fuzzy Logic Controllers 5. Self-Organising Fuzzy Logic Control 5.1 Introduction 5.2 Control Rule Base SOFLICs 5.3 Rule Based SOFLIC Applications 5.4 Systematic Design of Control Rule Based SOFLIC 6. Indirect Self-Organising Fuzzy Logic Controllers 6.1 Introduction 6.2 Self-Organising Fuzzy Models and Predictors 6.3 Relation Causality Inversion 6.4 Controller Design 6.5 Adaptive Fuzzy Controller 6.6 A Simulation Example of Indirect Adaptive Fuzzy Logic Control 6.7 Nested and Hybrid Fuzzy Controllers 7. Case Studies of Indirect Adaptive Fuzzy Control 7.1 Regulation of a Ship's Heading 7.2 Track Control of a City Bus 7.3 Autonomous Road Vehicle Control and Guidance 7.4 Observations on Indirect Fuzzy Adaptive Control 8. Neural Network Approximation Capability for Control and Modelling 8.1 Introduction 8.2 Approximation Capability of Artificial Neural Networks 8.3 Multilayer Perceptrons in Neurocontrol 8.4 Radial Basis Functions in Modelling and Control 9. The B-spline Neural Network and Fuzzy Logic 9.1 Introduction 9.2 Polynomial Basis Functions 9.3 B-splines for Guidance 9.4 Multivariate Basis Functions 9.5 Weighted Adaptation 9.6 B-spline Neural Net Nonlinear Time Series Predictors and Modelling 9.7 A Comparison between Fuzzy Logic and Single Layer Associative Memory Neural Networks 9.8 Conclusions Appendix: Mathematical Prerequisites A.1 Metric Spaces A.2 Normed Metric Spaces A.3 Algebras A.4 Approximation in Normed Spaces Contents

Item Type: Book
Divisions: Faculty of Physical Sciences and Engineering > Electronics and Computer Science > Comms, Signal Processing & Control
ePrint ID: 250227
Date Deposited: 04 May 1999
Last Modified: 27 Mar 2014 19:51
Publisher: World Scientific Press
Further Information:Google Scholar
URI: http://eprints.soton.ac.uk/id/eprint/250227

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