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6. Blind Equalization Algorithm Based on Evolutionary Neural Network

  • Liyi Zhang
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Blind Equalization in Neural Networks
This chapter is in the book Blind Equalization in Neural Networks

Abstract

This chapter includes the following contents: the basic theory and development of genetic algorithm (GA), the parameter coding, the initial population setting, the design of adapt function, genetic operator choice, the controlling parameter setting method, and the GA and neural network combination mechanism. Furthermore, blind equalization algorithm based on GA optimization neural network weights and structure is studied and computer simulation is carried out. The results show that the convergence performance of the two new algorithms is faster compared with the traditional neural network blind equalization algorithm.

Abstract

This chapter includes the following contents: the basic theory and development of genetic algorithm (GA), the parameter coding, the initial population setting, the design of adapt function, genetic operator choice, the controlling parameter setting method, and the GA and neural network combination mechanism. Furthermore, blind equalization algorithm based on GA optimization neural network weights and structure is studied and computer simulation is carried out. The results show that the convergence performance of the two new algorithms is faster compared with the traditional neural network blind equalization algorithm.

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/10.1515/9783110450293-006

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
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