Title:
Applied neural networks for signal processing
Personal Author:
Publication Information:
London : Cambridge University Press, 1998
ISBN:
9780521644006
Added Author:
Available:*
Library | Item Barcode | Call Number | Material Type | Item Category 1 | Status |
|---|---|---|---|---|---|
Searching... | 30000010029346 | TK5102.9 L86 1998 | Open Access Book | Book | Searching... |
Searching... | 30000010273222 | TK5102.9 L86 1998 | Open Access Book | Gift Book | Searching... |
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Summary
Summary
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.
Table of Contents
| Preface | p. ix |
| 1 Fundamental Models of Neural Networks for Signal Processing | p. 1 |
| 1.1 The Discrete-Time Hopfield Neural Network | p. 1 |
| 1.2 The Continuous-Time Hopfield Neural Network | p. 5 |
| 1.3 Cellular Neural Networks | p. 11 |
| 1.4 Multilayer Perceptron Networks | p. 16 |
| 1.5 Self-Organizing Systems | p. 20 |
| 1.6 Radial Basis Function Networks | p. 22 |
| 1.7 High-Order Neural Networks | p. 26 |
| Bibliography | p. 29 |
| 2 Neural Networks for Filtering | p. 32 |
| 2.1 Neural Networks for the Least-Squares Algorithm | p. 33 |
| 2.2 Neural Networks for the Recursion Least-Squares Algorithm | p. 45 |
| 2.3 Neural Networks for the Constrained Least-Squares Algorithm | p. 49 |
| 2.4 Neural Networks for the Total-Least-Squares Algorithm | p. 51 |
| 2.5 Neural Networks for a Class of Nonlinear Filters | p. 58 |
| 2.6 Neural Networks for General Nonlinear Filters | p. 61 |
| 2.6.1 Fundamentals | p. 61 |
| 2.6.2 An Application Example: Signal Prediction | p. 63 |
| 2.7 Neural Networks for Generalized Stack Filters | p. 65 |
| Bibliography | p. 71 |
| 3 Neural Networks for Spectral Estimation | p. 74 |
| 3.1 Maximum Entropy Spectral Estimation by Neural Networks | p. 74 |
| 3.2 Harmonic Retrieval by Neural Networks | p. 80 |
| 3.3 Neural Networks for Multichannel Spectral Estimation | p. 94 |
| 3.4 Neural Networks for Two-Dimensional Spectral Estimation | p. 108 |
| 3.5 Neural Networks for Higher-Order Spectral Estimation | p. 112 |
| Bibliography | p. 117 |
| 4 Neural Networks for Signal Detection | p. 121 |
| 4.1 A Likelihood-Ratio Neural Network Detector | p. 122 |
| 4.1.1 Fundamentals of the Likelihood-Ratio Detector | p. 122 |
| 4.1.2 Structure of the Likelihood-Ratio Neural Network Detector | p. 124 |
| 4.2 Neural Networks for Signal Detection in Non-Gaussian Noise | p. 126 |
| 4.3 Neural Networks for Pulse Signal Detection | p. 130 |
| 4.4 Neural Networks for Weak Signal Detection in High-Noise Environments | p. 134 |
| 4.5 Neural Networks for Moving-Target Detection | p. 138 |
| Bibliography | p. 150 |
| 5 Neural Networks for Signal Reconstruction | p. 152 |
| 5.1 Maximum Entropy Signal Reconstruction by Neural Networks | p. 153 |
| 5.2 Reconstruction of Binary Signals Using MLP Networks | p. 162 |
| 5.3 Reconstruction of Binary Signals Using RBF Networks | p. 168 |
| 5.4 Reconstruction of Binary Signals Using High-Order Neural Networks | p. 177 |
| 5.5 Blind Equalization Using Neural Networks | p. 181 |
| Bibliography | p. 185 |
| 6 Neural Networks for Adaptive Extraction of Principal and Minor Components | p. 188 |
| 6.1 Adaptive Extraction of the First Principal Component | p. 188 |
| 6.2 Adaptive Extraction of the Principal Subspace | p. 201 |
| 6.3 Adaptive Extraction of the Principal Components | p. 205 |
| 6.4 Adaptive Extraction of the Minor Components | p. 216 |
| 6.4.1 Adaptive Extraction of the First Minor Component | p. 217 |
| 6.4.2 Adaptive Extraction of the Multiple Minor Components | p. 223 |
| 6.5 Robust and Nonlinear PCA Algorithms and Networks | p. 229 |
| 6.6 Unsupervised Learning Algorithms of Higher-Order Statistics | p. 233 |
| Bibliography | p. 236 |
| 7 Neural Networks for Array Signal Processing | p. 240 |
| 7.1 Real-Time Implementation of Three DOA Estimation Methods Using Neural Networks | p. 241 |
| 7.1.1 The ML and Alternating Projection ML Methods | p. 241 |
| 7.1.2 The Propagator Method | p. 244 |
| 7.1.3 Real-Time Computation of the DOA Algorithms Using Neural Networks | p. 246 |
| 7.2 Neural Networks for the MUSIC Bearing Estimation Algorithm | p. 252 |
| 7.2.1 Computation of the Noise Subspace of the Repeated Smallest Eigenvalues | p. 254 |
| 7.2.2 Computation of the Noise Subspace in the General Case | p. 262 |
| 7.3 Neural Networks for the ML Bearing Estimation | p. 271 |
| 7.4 Hypothesis-Based Bearing Estimation Using Neural Networks | p. 280 |
| 7.5 Beamforming Using Neural Networks | p. 287 |
| Bibliography | p. 292 |
| 8 Neural Networks for System Identification | p. 294 |
| 8.1 Fundamentals of System Identification | p. 294 |
| 8.2 System Identification Using MLP Networks | p. 298 |
| 8.3 System Identification Using RBF Networks | p. 311 |
| 8.4 Recurrent Neural Networks for System Identification | p. 317 |
| 8.5 Neural Networks for Real-Time System Identification | p. 323 |
| 8.5.1 Neural Networks for Real-Time Identification of SISO Systems | p. 323 |
| 8.5.2 Neural Networks for Real-Time Identification of MIMO Systems | p. 327 |
| 8.6 Blind System Identification and Neural Networks | p. 329 |
| Bibliography | p. 332 |
| 9 Neural Networks for Signal Compression | p. 335 |
| 9.1 Neural Networks for Linear Predictive Coding | p. 336 |
| 9.2 MLP Networks for Nonlinear Predictive Coding | p. 342 |
| 9.3 High-Order Neural Networks for Nonlinear Predictive Coding | p. 346 |
| 9.4 Neural Networks for the Karhunen-Loeve Transform Coding | p. 350 |
| 9.5 Neural Networks for Wavelet Transform Coding | p. 355 |
| 9.6 Neural Networks for Vector Quantization | p. 358 |
| Bibliography | p. 362 |
| Index | p. 365 |
