Blind Equalization in Neural Networks : : Theory, Algorithms and Applications /

Blind Equalization in Neural Networks : : Theory, Algorithms and Applications / / Liyi Zhang.

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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...

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Bibliographic Details
Superior document:Title is part of eBook package: De Gruyter DG Plus eBook-Package 2018
VerfasserIn:
Zhang, Liyi,
TeilnehmendeR:
Tsinghua University Press,.
Place / Publishing House:Berlin ;, Boston : : De Gruyter, , [2017]
©2018
Year of Publication:2017
Language:English
Online Access:
Physical Description:1 online resource (XII, 256 p.)
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Table of Contents:
  • Frontmatter
  • 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

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