Applied Neural Networks for Signal Processing

The use of neural networks in signal processing is becoming increasingly widespread, with applications in many areas. Applied Neural Networks for Signal Processing is the first book to provide a comprehensive introduction to this broad field. It begins by covering the basic principles and models of neural networks in signal processing. The authors then discuss a number of powerful algorithms and architectures for a range of important problems, and describe practical implementation procedures. A key feature of the book is that many carefully designed simulation examples are included to help guide the reader in the development of systems for new applications. The book will be an invaluable reference for scientists and engineers working in communications, control or any other field related to signal processing. It can also be used as a textbook for graduate courses in electrical engineering and computer science.

Preface

ix

Fundamental Models of Neural Networks for Signal Processing

1

(31)

The discrete-Time Hopfield Neural Network

1

(4)

The Continuous-Time Hopfield Neural Network

5

(6)

Cellular Neural Networks

11

(5)

Multilayer Perceptron Networks

16

(4)

Self-Organizing Systems

20

(2)

Radial Basis Function Networks

22

(4)

High-Order Neural Networks

26

(6)

Bibliography

29

(3)

Neural Networks for Filtering

32

(42)

Neural Network for the Least-Squares Algorithm

33

(12)

Neural Networks for the Recursion Least-Squares Algorithm

45

(4)

Neural Networks for the Constrained Least-Squares Algorithm

49

(2)

Neural Networks for the Total-Least-Squares Algorithm

51

(7)

Neural Networks for a Class of Nonlinear Filters

58

(3)

Neural Networks for General Nonlinear Filters

61

(4)

Fundamentals

61

(2)

An Application Example: Signal Prediction

63

(2)

Neural Networks for Generalized Stack Filters

65

(9)

Bibliography

71

(3)

Neural Networks for Spectral Estimation

74

(47)

Maximum Entropy Spectral Estimation by Neural Networks

74

(6)

Harmonic Retrieval by Neural Networks

80

(14)

Neural Networks for Multichannel Spectral Estimation

94

(14)

Neural Networks for Two-Dimensional Spectral Estimation

108

(4)

Neural Networks for Higher-Order Spectral Estimation

112

(9)

Bibliography

117

(4)

Neural Networks for Signal Detection

121

(31)

A Likelihood-Ratio Neural Network Detector

122

(4)

Fundamentals of the Likelihood-Ratio Detector

122

(2)

Structure of the Likelihood-Ratio Neural Network Detector

124

(2)

Neural Networks for Signal Detection in Non-Gaussian Noise

126

(4)

Neural Networks for Pulse Signal Detection

130

(4)

Neural Networks for Weak Signal Detection in High-Noise Environments

134

(4)

Neural Networks for Moving-Target Detection

138

(14)

Bibliography

150

(2)

Neural Networks for Signal Reconstruction

152

(36)

Maximum Entropy Signal Reconstruction by Neural Networks

153

(9)

Reconstruction of Binary Signals Using MLP Networks

162

(6)

Reconstruction of Binary Signals Using RBF Networks

168

(9)

Reconstruction of Binary Signals Using High-Order Neural Networks

177

(4)

Blind Equalization Using Neural Networks

181

(7)

Bibliography

185

(3)

Neural Networks for Adaptive Extraction of Principal and Minor Components

188

(52)

Adaptive Extraction of the First Principal Component

188

(13)

Adaptive Extraction of the Principal Subspace

201

(4)

Adaptive Extraction of the Principal Components

205

(11)

Adaptive Extraction of the Minor Components

216

(13)

Adaptive Extraction of the First Minor Component

217

(6)

Adaptive Extraction of the Multiple Minor Components

223

(6)

Robust and Nonlinear PCA Algorithms and Networks

229

(4)

Unsupervised Learning Algorithms of Higher-Order Statistics

233

(7)

Bibliography

236

(4)

Neural Networks for Array Signal Processing

240

(54)

Real-Time Implementation of Three DOA Estimation Methods Using Neural Networks

241

(11)

The ML and Alternating Projection ML Methods

241

(3)

The Propagator Method

244

(2)

Real-Time Computation of the DOA Algorithms Using Neural Networks

246

(6)

Neural Networks for the MUSIC Bearing Estimation Algorithm

252

(19)

Computation of the Noise Subspace of the Repeated Smallest Eigenvalues

254

(8)

Computation of the Noise Subspace in the General Case

262

(9)

Neural Networks for the ML Bearing Estimation

271

(9)

Hypothesis-Based Bearing Estimation Using Neural Networks

280

(7)

Beamforming Using Neural Networks

287

(7)

Bibliography

292

(2)

Neural Networks for System Identification

294

(41)

Fundamentals of System Identification

294

(4)

System Identification Using MLP Networks

298

(13)

System Identification Using RBF Networks

311

(6)

Recurrent Neural Networks for System Identification

317

(6)

Neural Networks for Real-Time System Identification

323

(6)

Neural Networks for Real-Time Identification of SISO Systems

323

(4)

Neural Networks for Real-Time Identification of MIMO Systems

327

(2)

Blind System Identification and Neural Networks

329

(6)

Bibliography

332

(3)

Neural Networks for Signal Compression

335

(30)

Neural Networks for Linear Predictive Coding

336

(6)

MLP Networks for Nonlinear Predictive Coding

342

(4)

High-Order Neural Networks for Nonlinear Predictive Coding

346

(4)

Neural Networks for the Karhunen-Loeve Transform Coding

350

(5)

Neural Networks for Wavelet Transform Coding

355

(3)

Neural Networks for Vector Quantization

358

(7)

Bibliography

362

(3)

Index

365

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Applied Neural Networks for Signal Processing

Summary

Table of Contents

Supplemental Materials

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Applied Neural Networks for Signal Processing

Summary

Table of Contents

Supplemental Materials

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