Blind Equalization in Neural Networks

Blind Equalization in Neural Networks

by Liyi Zhang, Tsinghua University Tsinghua University Press
Released December 2017
Publisher(s): De Gruyter
ISBN: 9783110449679

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Book description

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.

Table of contents

  1. Cover
  2. Title Page
  3. Copyright
  4. Preface
  5. Contents
  6. 1 Introduction
    1. 1.1 The research significance of the BE technology
    2. 1.2 The application of BE technology
      1. 1.2.1 The application in digital television
      2. 1.2.2 The application in CATV system
      3. 1.2.3 The application in smart antenna
      4. 1.2.4 The application in software radio
      5. 1.2.5 The application in blind image restoration
      6. 1.2.6 The application in radiofrequency identification
    3. 1.3 The research progress of neural network BE algorithm
      1. 1.3.1 The FFNN BE algorithm
      2. 1.3.2 The BE algorithm based on FBNN
      3. 1.3.3 The FNN BE algorithm
      4. 1.3.4 The ENN BE algorithm
      5. 1.3.5 BE algorithm based on WNN
    4. 1.4 The research background and structure
      1. 1.4.1 The research background
      2. 1.4.2 The structure of the book
  7. 2 The Fundamental Theory of Neural Network Blind Equalization Algorithm
    1. 2.1 The fundamental principle of blind equalization
      1. 2.1.1 The concept of blind equalization
      2. 2.1.2 The structure of blind equalizer
      3. 2.1.3 The basic algorithm of blind equalization
      4. 2.1.4 The equalization criteria of blind equalization
    2. 2.2 The fundamental theory of neural network
      1. 2.2.1 The concept of artificial neural network
      2. 2.2.2 The development of ANN
      3. 2.2.3 The characteristics of ANN
      4. 2.2.4 Structure and classification of ANN
    3. 2.3 The fundamental principle of neural network blind equalization algorithm
      1. 2.3.1 The principle of blind equalization algorithm based on neural network filter
      2. 2.3.2 The principle of blind equalization algorithm based on neural network controller
      3. 2.3.3 The principle of blind equalization algorithm based on neural network controller classifier
    4. 2.4 The learning method of neural network blind equalization algorithm
      1. 2.4.1 The BP algorithm
      2. 2.4.2 The improved BP algorithm
    5. 2.5 The evaluation index of the neural network blind equalization algorithm
      1. 2.5.1 Convergence speed
      2. 2.5.2 The computational complexity
      3. 2.5.3 The bit error characteristics
      4. 2.5.4 The ability of tracking the time-varying channel
      5. 2.5.5 The ability of anti interference
      6. 2.5.6 The convexity of the cost function
      7. 2.5.7 The steady-state residual error
    6. 2.6 Summary
  8. 3 Research of Blind Equalization Algorithms Based on FFNN
    1. 3.1 Basic principles of FFNN
      1. 3.1.1 Concept of FFNN
      2. 3.1.2 Structure of FFNN
      3. 3.1.3 Characteristics of FFNN
    2. 3.2 Blind equalization algorithm based on the three-layer FFNN
      1. 3.2.1 Model of the three-layer FFNN
      2. 3.2.2 Real blind equalization algorithm based on the three-layer FFNN
      3. 3.2.3 Complex blind equalization algorithm based on the three-layer FFNN
    3. 3.3 Blind equalization algorithm based on the multilayer FFNN
      1. 3.3.1 Concept of the multilayer FFNN
      2. 3.3.2 Blind equalization algorithm based on the four-layer FFNN
      3. 3.3.3 Blind equalization algorithm based on the five-layer FFNN
    4. 3.4 Blind equalization algorithm based on the momentum term FFNN
      1. 3.4.1 Basic principles of algorithm
      2. 3.4.2 Derivation of algorithm
      3. 3.4.3 Computer simulation results
    5. 3.5 Blind equalization algorithm based on the time-varying momentum term FFNN
      1. 3.5.1 Basic principles of algorithm
      2. 3.5.2 Derivation of algorithm
      3. 3.5.3 Computer simulation results
    6. 3.6 Blind equalization algorithm based on variable step-size FFNN
      1. 3.6.1 Basic principles of algorithm
      2. 3.6.2 Derivation of algorithm
      3. 3.6.3 Computer simulation results
    7. 3.7 Summary
  9. 4 Research of Blind Equalization Algorithms Based on the FBNN
    1. 4.1 Basic principles of FBNN
      1. 4.1.1 Concept of FBNN
      2. 4.1.2 Structure of FBNN
      3. 4.1.3 Characteristics of FBNN
    2. 4.2 Blind equalization algorithm based on the bilinear recurrent NN
      1. 4.2.1 Basic principles of algorithm
      2. 4.2.2 Real blind equalization algorithm based on BLRNN
      3. 4.2.3 Complex blind equalization algorithm based on BLRNN
    3. 4.3 Blind equalization algorithm based on the diagonal recurrent NN
      1. 4.3.1 Model of diagonal recurrent NN
      2. 4.3.2 Derivation of algorithm
      3. 4.3.3 Computer simulation results
    4. 4.4 Blind equalization algorithm based on the quasi-DRNN
      1. 4.4.1 Model of quasi-DRNN
      2. 4.4.2 Derivation of algorithm
      3. 4.4.3 Computer simulation results
    5. 4.5 Blind equalization algorithm based on the variable step-size DRNN
      1. 4.5.1 Basic principles of algorithm
      2. 4.5.2 Derivation of algorithm
      3. 4.5.3 Computer simulation results
    6. 4.6 Blind equalization algorithm based on the variable step-size QDRNN
      1. 4.6.1 Basic principles of algorithm
      2. 4.6.2 Derivation of algorithm
      3. 4.6.3 Computer simulation results
    7. 4.7 Summary
  10. 5 Research of Blind Equalization Algorithms Based on FNN
    1. 5.1 Basic principles of FNN
      1. 5.1.1 Concept of FNN
      2. 5.1.2 Structure of FNN
      3. 5.1.3 The choice of fuzzy membership function
      4. 5.1.4 Learning algorithm of FNN
      5. 5.1.5 Characteristics of FNN
    2. 5.2 Blind equalization algorithm based on the FNN filter
      1. 5.2.1 Basic principles of algorithm
      2. 5.2.2 Derivation of algorithm
      3. 5.2.3 Computer simulation results
    3. 5.3 Blind equalization algorithm based on the FNN controller
      1. 5.3.1 Basic principles of the algorithm
      2. 5.3.2 Derivation of algorithm
      3. 5.3.3 Computer simulation results
    4. 5.4 Blind equalization algorithm based on the FNN classifier
      1. 5.4.1 Basic principles of algorithm
      2. 5.4.2 Derivation of algorithm
      3. 5.4.3 Computer simulation results
    5. 5.5 Summary
  11. 6 Blind Equalization Algorithm Based on Evolutionary Neural Network
    1. 6.1 Basic principles of evolutionary neural networks
      1. 6.1.1 The concept of GA
      2. 6.1.2 Development of GA
      3. 6.1.3 GA parameters
      4. 6.1.4 The basic process of GA
      5. 6.1.5 Characteristics of GA
      6. 6.1.6 The integration mechanism of GAand neural network
    2. 6.2 Blind equalization algorithm based on neural network weights optimized byGA
      1. 6.2.1 The basic algorithm principle
      2. 6.2.2 Feed-forward neural network weights optimized by GA blind equalization algorithm (GA-FFNNW) in binary encoding
      3. 6.2.3 Real encoding GA-FFNNW blind equalization algorithm
    3. 6.3 GA optimization neural network structure blind equalization algorithm (GA-FFNNS)
      1. 6.3.1 The basic algorithm principle
      2. 6.3.2 Algorithm derivation
      3. 6.3.3 Computer simulation
    4. 6.4 Summary
  12. 7 Blind equalization Algorithm Based on Wavelet Neural Network
    1. 7.1 Basic principle of wavelet neural network
      1. 7.1.1 The concept of wavelet neural network
      2. 7.1.2 The structure of wavelet neural network
      3. 7.1.3 The characteristics of wavelet neural network
    2. 7.2 Blind equalization algorithm based on feed-forward wavelet neural network
      1. 7.2.1 Algorithm principle
      2. 7.2.2 Blind equalization algorithm based on feed-forward wavelet neural network in real number system
      3. 7.2.3 Blind equalization algorithm based on FFWNN in complex number system
    3. 7.3 Blind equalization algorithm based on recurrent wavelet neural network
      1. 7.3.1 Principle of algorithm
      2. 7.3.2 Blind equalization algorithm based on BLRWNN in real number system
      3. 7.3.3 Blind equalization algorithm based on BLRWNN in complex system
    4. 7.4 Summary
  13. 8 Application of Neural Network Blind Equalization Algorithm in Medical Image Processing
    1. 8.1 Concept of image blind equalization
      1. 8.1.1 Imaging mechanism and degradation process of medical CT image
      2. 8.1.2 The basic principle of medical CT images blind equalization
      3. 8.1.3 Quantitative measurements
    2. 8.2 Medical CT image neural network blind equalization algorithm based on Zigzag
      1. 8.2.1 Basic principle of algorithm
      2. 8.2.2 Iterative formula derivation
      3. 8.2.3 Analysis of algorithm convergence performance
      4. 8.2.4 Experimental simulation
    3. 8.3 Medical CT image neural network blind equalization algorithm based on double zigzag encoding
      1. 8.3.1 The basic principle of algorithm
      2. 8.3.2 Formula derivation of iterative algorithm
      3. 8.3.3 Experimental simulation
    4. 8.4 Summary
  14. Appendix A: Derivation of the Hidden Layer Weight Iterative Formula in the Blind Equalization Algorithm Based on the Complex Three-Layer FFNN
  15. Appendix B: Iterative Formulas Derivation of Complex Blind Equalization Algorithm Based on BRNN
  16. Appendix C: Types of Fuzzy Membership Function
  17. Appendix D: Iterative Formula Derivation of Blind Equalization Algorithm Based on DRFNN
  18. References
  19. Index

Product information

  • Title: Blind Equalization in Neural Networks
  • Author(s): Liyi Zhang, Tsinghua University Tsinghua University Press
  • Release date: December 2017
  • Publisher(s): De Gruyter
  • ISBN: 9783110449679