Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models (Hardcover)

This textbook provides a thorough introduction to the field of learning from experimental data and soft computing. Support vector machines (SVM) and neural networks (NN) are the mathematical structures, or models, that underlie learning, while fuzzy logic systems (FLS) enable us to embed structured human knowledge into workable algorithms. The book assumes that it is not only useful, but necessary, to treat SVM, NN, and FLS as parts of a connected whole. Throughout, the theory and algorithms are illustrated by practical examples, as well as by problem sets and simulated experiments. This approach enables the reader to develop SVM, NN, and FLS in addition to understanding them. The book also presents three case studies: on NN-based control, financial time series analysis, and computer graphics. A solutions manual and all of the MATLAB programs needed for the simulated experiments are available.

Contents

Preface
Introduction

1.Learning and Soft Computing: Rationale, Motivations, Needs, Basics

2.Support Vector Machines

3.Single-Layer Networks

4.Multilayer Perception

5.Radial Basis Function Networks

6.Fuzzy Logic Systems

7.Case Studies

8.Basic Nonlinear Optimization Methods

9.Mathematical Tools of Soft computing

Selected Abbreviations

Notes

References

Index