Home Mathematics 5. Research of Blind Equalization Algorithms Based on FNN
Chapter
Licensed
Unlicensed Requires Authentication

5. Research of Blind Equalization Algorithms Based on FNN

  • Liyi Zhang
Become an author with De Gruyter Brill
Explore this Subject How to publish with us
Blind Equalization in Neural Networks
This chapter is in the book Blind Equalization in Neural Networks

Abstract

In this chapter, the concept, development, structures, learning algorithms, and characteristics of the fuzzy neural network (FNN) are summarized. The methods of how to select and determine the fuzzy membership function are introduced. The blind equalization algorithms based on FNN filter, FNN controller, and FNN classifier are researched. The structures adopt the dynamic recurrent FNN, five-layer FNN, and three-layer FNN, respectively. The iterative formulas of algorithms are deduced. The simulation results verify the effectiveness of the proposed algorithms.

Abstract

In this chapter, the concept, development, structures, learning algorithms, and characteristics of the fuzzy neural network (FNN) are summarized. The methods of how to select and determine the fuzzy membership function are introduced. The blind equalization algorithms based on FNN filter, FNN controller, and FNN classifier are researched. The structures adopt the dynamic recurrent FNN, five-layer FNN, and three-layer FNN, respectively. The iterative formulas of algorithms are deduced. The simulation results verify the effectiveness of the proposed algorithms.

Chapters in this book

  1. Frontmatter I
  2. Preface V
  3. Contents VII
  4. 1. Introduction 1
  5. 2. The Fundamental Theory of Neural Network Blind Equalization Algorithm 17
  6. 3. Research of Blind Equalization Algorithms Based on FFNN 51
  7. 4. Research of Blind Equalization Algorithms Based on the FBNN 94
  8. 5. Research of Blind Equalization Algorithms Based on FNN 124
  9. 6. Blind Equalization Algorithm Based on Evolutionary Neural Network 149
  10. 7. Blind equalization Algorithm Based on Wavelet Neural Network 180
  11. 8. Application of Neural Network Blind Equalization Algorithm in Medical Image Processing 205
  12. Appendix A: Derivation of the Hidden Layer Weight Iterative Formula in the Blind Equalization Algorithm Based on the Complex Three-Layer FFNN 229
  13. Appendix B: Iterative Formulas Derivation of Complex Blind Equalization Algorithm Based on BRNN 231
  14. Appendix C: Types of Fuzzy Membership Function 235
  15. Appendix D: Iterative Formula Derivation of Blind Equalization Algorithm Based on DRFNN 239
  16. References 243
  17. Index 251
https://doi.org/10.1515/9783110450293-005

Chapters in this book

  1. Frontmatter I
  2. Preface V
  3. Contents VII
  4. 1. Introduction 1
  5. 2. The Fundamental Theory of Neural Network Blind Equalization Algorithm 17
  6. 3. Research of Blind Equalization Algorithms Based on FFNN 51
  7. 4. Research of Blind Equalization Algorithms Based on the FBNN 94
  8. 5. Research of Blind Equalization Algorithms Based on FNN 124
  9. 6. Blind Equalization Algorithm Based on Evolutionary Neural Network 149
  10. 7. Blind equalization Algorithm Based on Wavelet Neural Network 180
  11. 8. Application of Neural Network Blind Equalization Algorithm in Medical Image Processing 205
  12. Appendix A: Derivation of the Hidden Layer Weight Iterative Formula in the Blind Equalization Algorithm Based on the Complex Three-Layer FFNN 229
  13. Appendix B: Iterative Formulas Derivation of Complex Blind Equalization Algorithm Based on BRNN 231
  14. Appendix C: Types of Fuzzy Membership Function 235
  15. Appendix D: Iterative Formula Derivation of Blind Equalization Algorithm Based on DRFNN 239
  16. References 243
  17. Index 251
Sign up now to receive a 20% welcome discount
Institutional Access
How does access work?
Have an idea on how to improve our website?
Please write us.
© 2025 De Gruyter Brill
Downloaded on 27.9.2025 from https://www.degruyterbrill.com/document/doi/10.1515/9783110450293-005/html
Scroll to top button