Blind Equalization in Neural Networks: Theory, Algorithms and Applications

✍ Scribed by Liyi Zhang; Tsinghua University Press

• Publisher: De Gruyter
• Year: 2017
• Tongue: English
• Leaves: 268
• Category: Library

✦ Synopsis

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.

• Summarizes blind equalization algorithms in various neural networks.
• Illustrates theoretical studies with simulations and examples.
• Concise and handy to use.
  

✦ Table of Contents

Preface
Contents
1. Introduction
2. The Fundamental Theory of Neural Network Blind Equalization Algorithm
3. Research of Blind Equalization Algorithms Based on FFNN
4. Research of Blind Equalization Algorithms Based on the FBNN
5. Research of Blind Equalization Algorithms Based on FNN
6. Blind Equalization Algorithm Based on Evolutionary Neural Network
7. Blind equalization Algorithm Based on Wavelet Neural Network
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
References
Index