Blind Equalization in Neural Networks: Theory, Algorithms and Applications

by Liyi Zhang, Tsinghua University Press (Contribution by)

Overview

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.

Product Details

ISBN-13:	9783110449679
Publisher:	De Gruyter
Publication date:	12/18/2017
Sold by:	Barnes & Noble
Format:	eBook
Pages:	268
File size:	36 MB
Note:	This product may take a few minutes to download.
Age Range:	18 Years

About the Author

Liyi Zhang, Tianjin University of Commerce, Tianjin, China.

Table of Contents

Table of ContentChapter 1 Introduction 1.1 Significance of Blind Equalization1.2 Application of Blind Equalization 1.2.1 Application in Digital TV1.2.2 Application in CATV System 1.2.3 Application in Smart Antenna1.2.4 Application in Software Radio1.2.5 Application in Blind Image Restoration1.2.6 Application in RFID 1.3 Progress of Neural Network Blind Equalization Algorithm1.3.1 Feedforward Neural Network Blind Equalization Algorithm1.3.2 Feedback Neural Network Blind Equalization Algorithm51.3.3 Fuzzy Neural Network Blind Equalization Algorithm1.3.4 Evolutionary Neural Network Blind Equalization Algorithm 1.3.5 Wavelet Neural Network Blind Equalization Algorithm1.4 Research Background and Structural Arrangements1.4.1 Background1.4.2 Structural Arrangement of BookChapter 2 Principle of Neural Network Blind Equalization Algorithm2.1 Basic Principles of Blind Equalization2.1.1 Concept of Blind Equalization2.1.2 Structure of Blind Equalizer2.1.3 Basic Blind Equalization Algorithm2.1.4 Equalization Criteria of Blind Equalization2.2 Theory of Neural Networks2.2.1 Concept of Artificial Neural Networks2.2.2 Development of Artificial Neural Networks2.2.3 Features of Artificial Neural Networks2.2.4 Structure and Classification of Artificial Neural Networks2.3 Basic Principles of Neural Network Blind Equalization Algorithm2.3.1 Blind Equalization Algorithm Based on Neural Network Filter2.3.2 Blind Equalization Algorithm Based on Neural Network Controller2.3.3 Blind Equalization Algorithm Based on Neural Network Classifier2.4 Learning Methods of Neural Network Blind Equalization Algorithm2.4.1 BP Algorithm2.4.2 Improved BP Algorithm2.5 Evaluation of Neural Network Blind Equalization Algorithm2.5.1 Convergence Rate2.5.2 Computational Complexity2.5.3 BER Performance2.5.4 The Ability Tracking Time-varying Channel2.5.5 Anti-jamming Capability2.5.6 Convexity of Cost Function2.5.7 State Residual Error2.6 SummaryChapter 3 Research of Feedforward Neural Network Blind Equalization Algorithm3.1 Basic Principles of Feedforward Neural Networks3.1.1 Concept of Feedforward Neural Networks3.1.2 Structure of Feedforward Neural Networks3.1.3 Features of Feedforward Neural Networks3.2 Blind Equalization Algorithm Based on Three-layered Feedforward Neural Networks3.2.1 Model of Three-layered Feedforward Neural Networks3.2.2 Real Blind Equalization Algorithm Based on Three-layered Feedforward Neural Networks 3.2.3 Complex Blind Equalization Algorithm Based on Three-layered Feedforward Neural Networks3.3 Blind Equalization Algorithm Based on Multi-layered Feedforward Neural Networks3.3.1 Concept of Multi-layered Feedforward Neural Networks3.3.2 Blind Equalization Algorithm Based on Four-layered Feedforward Neural Networks3.3.3 Blind Equalization Algorithm Based on Five-layered Feedforward Neural Networks3.4 Blind Equalization Algorithm Based on Momentum Feedforward Neural Networks3.4.1 Basic Principles of Algorithm3.4.2 Derivation of Algorithm3.4.3 Computer Simulations3.5 Blind Equalization Algorithm Based on Time-varying Momentum Feedforward Neural Networks3.5.1 Basic Principles of Algorithm3.5.2 Derivation of Algorithm3.5.3 Computer Simulations3.6 Blind Equalization Algorithm Based on Variable Step-size Feedforward Neural Networks3.6.1 Basic Principles of Algorithm3.6.2 Derivation of Algorithm3.6.3 Computer Simulations3.7 SummaryAppendix I: Hidden Layer Weight Iteration Formula Derivation of Complex Blind Equalization Algorithm Based on Three-layered Feedforward Neural NetworksChapter 4 Research of Feedback Neural Network Blind Equalization Algorithm4.1 Basic Principles of Feedback Neural Networks4.1.1 Concept of Feedback Neural Networks4.1.2 Structure of Feedback Neural Networks4.1.3 Features of Feedback Neural Networks4.2 Blind Equalization Algorithm Based on Bilinear Feedback Neural Networks4.2.1 Model of Bilinear Feedback Neural Networks4.2.2 Real Blind Equalization Algorithm Based on Bilinear Feedback Neural Networks 4.2.3 Complex Blind Equalization Algorithm Based on Bilinear Feedback Neural Networks4.3 Blind Equalization Algorithm Based on Diagonal Recurrent Neural Networks4.3.1 Model of Diagonal Recurrent Neural Networks4.3.2 Derivation of Algorithm4.3.3 Computer Simulations4.4 Blind Equalization Algorithm Based on Quasi-diagonal Recurrent Neural Networks4.4.1 Model of Quasi-diagonal Recurrent Neural Networks4.4.2 Derivation of Algorithm4.4.3 Computer Simulations4.5 Blind Equalization Algorithm Based on Variable Step-size Diagonal Recurrent Neural Networks4.5.1 Basic Principles of Algorithm4.5.2 Derivation of Algorithm4.5.3 Computer Simulations4.6 Blind Equalization Algorithm Based on Variable Step-size Quasi-diagonal Recurrent Neural Networks4.6.1 Basic Principles of Algorithm4.6.2 Derivation of Algorithm4.6.3 Computer Simulations4.7 SummaryAppendix I: Iteration Formula Derivation of Complex Blind Equalization Algorithm Based on Bilinear Feedback Neural NetworksChapter 5 Research of Fuzzy Neural Network Blind Equalization Algorithm5.1 Basic Principles of Fuzzy Neural Networks5.1.1 Concept of Fuzzy Neural Networks5.1.2 Structure of Fuzzy Neural Networks5.1.3 Choosing Fuzzy Membership Functions5.1.4 Learning Algorithms of Fuzzy Neural Networks5.1.5 Features of Fuzzy Neural Networks5.2 Blind Equalization Algorithm Based on Fuzzy Neural Network Filter5.2.1 Basic Principles of Algorithm5.2.2 Derivation of Algorithm5.2.3 Computer Simulations5.3 Blind Equalization Algorithm Based on Fuzzy Neural Network Controller5.3.1 Basic Principles of Algorithm5.3.2 Derivation of Algorithm5.3.3 Computer Simulations5.4 Blind Equalization Algorithm Based on Fuzzy Neural Network Classifier5.4.1 Basic Principles of Algorithm5.4.2 Derivation of Algorithm5.4.3 Computer Simulations5.5 SummaryAppendix I: Types of Fuzzy Membership FunctionsAppendix II: Iteration Formula Derivation of Blind Equalization Algorithm Based on Dynamic Recurrent Fuzzy Neural NetworksChapter 6 Research of Evolutionary Neural Network Blind Equalization Algorithm6.1 Basic Principles of Evolutionary Neural Networks6.1.1 Concept of Genetic Algorithm6.1.2 Development of Genetic Algorithm6.1.3 Parameters of Genetic Algorithm6.1.4 Basic Process of Genetic Algorithm6.1.5 Features of Genetic Algorithm6.1.6 Combination of Genetic Algorithm and Neural Networks6.2 Neural Network Weight Optimization Blind Equalization Algorithm Using GA6.2.1 Basic Principles of Algorithm6.2.2 Neural Network Weight Optimization Blind Equalization Algorithm Using Binary Coding GA6.2.3 Neural Network Weight Optimization Blind Equalization Algorithm Using Real Coding GA6.3 Neural Network Structure Optimization Blind Equalization Algorithm Using GA6.3.1 Basic Principles of Algorithm6.3.2 Derivation of Algorithm6.3.3 Computer Simulations6.4 SummaryChapter 7 Research of Wavelet Neural Network Blind Equalization Algorithm7.1 Basic Principles of Wavelet Neural Networks7.1.1 Concept of Wavelet Neural Networks7.1.2 Structure of Wavelet Neural Networks7.1.3 Features of Wavelet Neural Networks47.2 Blind Equalization Algorithm Based on Feedforward Wavelet Neural Networks7.2.1 Basic Principles of Algorithm7.2.2 Real Blind Equalization Algorithm Based on Feedforward Wavelet Neural Networks7.2.3 Complex Blind Equalization Algorithm Based on Feedforward Wavelet Neural Networks7.3 Blind Equalization Algorithm Based on Feedback Wavelet Neural Networks7.3.1 Basic Principles of Algorithm37.3.2 Real Blind Equalization Algorithm Based on Feedback Neural Networks7.3.3 Complex Blind Equalization Algorithm Based on Feedback NeuralNetworks7.4 SummaryChapter 8 Application of Neural Network Blind Equalization Algorithm in Medical Image Processing8.1 Concept of Image Blind Equalization8.1.1 Imaging Mechanism and Degradation Process of Medical CT Image8.1.2 Basic Principles of Medical CT Image Blind Equalization8.1.3 Quantitative Measurement of Medical Image Blind Equalization8.2 Medical CT Image Neural Network Blind Equalization Algorithm Based on Zigzag Coding8.2.1 Basic Principles of Algorithm8.2.2 teration Formula Derivation of Algorithm8.2.3 Convergence Analysis of Algorithm8.2.4 Computer Simulations8.3 Medical CT Image Neural Network Blind Equalization Algorithm Based on Double Zigzag Coding8.3.1 Basic Principles of Algorithm8.3.2 Iteration Formula Derivation of Algorithm8.3.3 Computer Simulations8.4 SummaryReferences