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2. The Fundamental Theory of Neural Network Blind Equalization Algorithm

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Blind Equalization in Neural Networks
Ein Kapitel aus dem Buch Blind Equalization in Neural Networks

Abstract

In this chapter, the concept, structure, algorithm form, and equalization criterion of the blind equalization are first introduced. And then, the fundamental principle and learning method of the neural network blind equalization are expounded. Then, in order to overcome the shortcomings of traditional BP algorithms, some improved methods are summarized. Finally, the evaluation indexes of the blind equalization algorithm are analyzed. Among these evaluation indexes, the convex of cost function and steady residual error are analyzed emphatically.

Abstract

In this chapter, the concept, structure, algorithm form, and equalization criterion of the blind equalization are first introduced. And then, the fundamental principle and learning method of the neural network blind equalization are expounded. Then, in order to overcome the shortcomings of traditional BP algorithms, some improved methods are summarized. Finally, the evaluation indexes of the blind equalization algorithm are analyzed. Among these evaluation indexes, the convex of cost function and steady residual error are analyzed emphatically.

Kapitel in diesem Buch

  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/10.1515/9783110450293-002

Kapitel in diesem Buch

  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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