New Neurofuzzy Book

[Martin Brown]

Could you please post this announcement about our following book which
may be of interest to workers in the neurofuzzy field.

NEUROFUZZY ADAPTIVE MODELLING AND CONTROL,
Martin Brown and Chris Harris (University of Southampton, UK),
Prentice Hall, Hemel Hempstead, UK, 1994.
13-134453-6
Price: 29.95 UK pounds or 49.95 US dollars (Hardback).

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.

Contents:
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