Neurofuzzy Adaptive Modelling and Control

Brown, M. and Harris, C.J. (1994) Neurofuzzy Adaptive Modelling and Control, Prentice Hall


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This book provides a unified description of several adaptive neural and fuzzy networks and introduces the associative memory class of systems - which describe the similarities and differences existing between fuzzy and neural algorithms. Three networks are described in detail - the Albus CMAC, the B-spline network and a class of fuzzy systems - and then analysed, their desirable features (local learning, linearly dependent on the parameter set, fuzzy interpretation) are emphasised and the algorithms are all evaluated on a common time series problem and applied to a common ship control benchmark. Chapters: 1. An Introduction to Learning Modelling and Control 1.1 Preliminaries 1.2 Intelligent Control 1.3 Learning Modelling and Control 1.4 Artificial Neural Networks 1.5 Fuzzy Control Systems 1.6 Book Description 2. Neural Networks for Modelling and Control 2.1 Introduction 2.2 Neuromodelling and Control Architectures 2.3 Neural Network Structure 2.4 Training Algorithms 2.5 Validation of a Neural Model 2.6 Discussion 3. Associative Memory Networks 3.1 Introduction 3.2 A Common Description 3.3 Five Associative Memory Networks 3.4 Summary 4. Adaptive Linear Modelling 4.1 Introduction 4.2 Linear Models 4.3 Performance of the Model 4.4 Gradient Descent 4.5 Multi-Layer Perceptrons and Back Propagation 4.6 Network Stability 4.7 Conclusion 5. Instantaneous Learning Algorithms 5.1 Introduction 5.2 Instantaneous Learning Rules 5.3 Parameter Convergence 5.4 The Effects of Instantaneous Estimates 5.5 Learning Interference in Associative Memory Networks 5.6 Higher Order Learning Rules 5.7 Discussion 6. The CMAC Algorithm 6.1 Introduction 6.2 The Basic Algorithm 6.3 Adaptation Strategies 6.4 Higher Order Basis Functions 6.5 Computational Requirements 6.6 Nonlinear Time Series Modelling 6.7 Modelling and Control Applications 6.8 Conclusions 7. The Modelling Capabilities of the Binary CMAC 7.1 Modelling and Generalisation in the Binary CMAC 7.2 Measuring the Flexibility of the Binary CMAC 7.3 Consistency Equations 7.4 Orthogonal Functions 7.5 Bounding the Modelling Error 7.6 Investigating the CMAC's Coarse Coding Map 7.7 Conclusion 8. Adaptive B-spline Networks 8.1 Introduction 8.2 Basic Algorithm 8.3 B-spline Learning Rules 8.4 B-spline Time Series Modelling 8.5 Model Adaptation Rules 8.6 ASMOD Time Series Modelling 8.7 Discussion 9. B-spline Guidance Algorithms 9.1 Introduction 9.2 Autonomous Docking 9.3 Constrained Trajectory Generation 9.4 B-spline Interpolants 9.5 Boundary and Kinematic Constraints 9.6 Example: A Quadratic Velocity Interpolant 9.7 Discussion 10. The Representation of Fuzzy Algorithms 10.1 Introduction: How Fuzzy is a Fuzzy Model? 10.2 Fuzzy Algorithms 10.3 Fuzzy Sets 10.4 Logical Operators 10.5 Compositional Rule of Inference 10.6 Defuzzification 10.7 Conclusions 11. Adaptive Fuzzy Modelling and Control 11.1 Introduction 11.2 Learning Algorithms 11.3 Plant Modelling 11.4 Indirect Fuzzy Control 11.5 Direct Fuzzy Control References. Appendix A. Modified Error Correction Rule Appendix B. Improved CMAC Displacement Tables Appendix C. Associative Memory Network Software Structure C.1 Data Structures C.2 Interface Functions C.3 Sample C Code Appendix D. Fuzzy Intersection Appendix E. Weight to Rule Confidence Vector Map For further information about this book (mailing/shipping costs etc.) and other neurofuzzy titles in the Prentice Hall series please contact: LIZ DICKINSON Prentice Hall Paramount Publishing International Campus 400 Maylands Avenue Hemel Hempstead, HP2 7EZ United Kingdom Tel: 0442 881900 Fax: 0442 257115 Contents

Item Type: Book
Additional Information: Address: Hemel Hempstead
Divisions: Faculty of Physical Sciences and Engineering > Electronics and Computer Science > Comms, Signal Processing & Control
ePrint ID: 250255
Date Deposited: 04 May 1999
Last Modified: 27 Mar 2014 19:51
Publisher: Prentice Hall
Further Information:Google Scholar

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