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.
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
