Blind Equalization in Neural Networks

The book begins with an introduction of blind equalization theory and its application in neural networks, then discusses the algorithms in recurrent networks, fuzzy networks and other frequently-studied neural networks. Each algorithm is accompanied by derivation, modeling and simulation, making the book an essential reference for electrical engineers, computer intelligence researchers and neural scientists.

Table of Contents

Chapter 1 Introduction
Chapter 2 The Fundamental Theory Of Neural Network Blind Equalization Algorithm
Chapter 3 Research Of Blind Equalization Algorithms Based On Ffnn
Chapter 4 Research Of Blind Equalization Algorithms Based On The Fbnn
Chapter 5 Research Of Blind Equalization Algorithms Based On Fnn
Chapter 6 Blind Equalization Algorithm Based On Evolutionary Neural Network
Chapter 7 Blind Equalization Algorithm Based On Wavelet Neural Network
Chapter 8 Application Of Neural Network Blind Equalization Algorithm In Medical Image Processing
Appendix A: Derivation of the Hidden Layer Weight Iterative Formula in the Blind Equalization Algorithm Based on the Complex Three-Layer FFNN
Appendix B: Iterative Formulas Derivation of Complex Blind Equalization Algorithm Based on BRNN
Appendix C: Types of Fuzzy Membership Function
Appendix D: Iterative Formula Derivation of Blind Equalization Algorithm Based on DRFNN