Digital neural networks:
Saved in:
| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Englewood Cliffs, NJ
Prentice-Hall
1993
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| Series: | Prentice-Hall information and system sciences series
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| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | XVIII, 444 S. graph. Darst. |
| ISBN: | 0136123260 |
Staff View
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| 100 | 1 | |a Kung, Sun Y. |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Digital neural networks |c S. Y. Kung |
| 264 | 1 | |a Englewood Cliffs, NJ |b Prentice-Hall |c 1993 | |
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Record in the Search Index
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| adam_text | Digital
Neural
Networks
S Y Kung
Department of Electrical Engineering
Princeton University
pjg PTR Prentice Hall
Englewood Cliffs, New Jersey 07632
Contents
Preface xv
PARTI INTRODUCTION_!
1 Overview 1
1 1 Introduction 1
111 Biological-Type Neural Networks 2
112 Application-Driven Neural Networks 4
1 2 Applications, Algorithms, and Architectures 6
121 Application Paradigms of Neural Models 7
122 Algorithmic Study on Neural Networks 11
123 Architectures of Neural Networks 14
124 Total Information Processing Systems 16
125 Representation and Feature Extraction 21
1 3 Taxonomy of Neural Networks 24
131 Supervised and Unsupervised Networks 25
132 Basis Function and Activation Function 27
133 Structures of Neural Networks 30
134 Mutual and Individual Training Strategies 33
135 Static and Temporal Pattern Recognitions 36
136 Decision and Approximation/Optimization Formulations 38
1 4 Concluding Remarks 39
1 5 Problems 40
vii
vffi Contents
PART II UNSUPERVISED MODELS
2 Fixed-Weight Associative Memory Networks 43
2 1 Introduction 43
2 2 Feedforward Associative Memory Networks 44
221 Linear Associative Memory 44
222 Nonlinear Associative Memory for Holographic
Retrieval 48
223 Hamming Networks 50
2 3 Feedback Associative Memory Networks 51
D231 Sequential (Asynchronous) Hopfield Model 52
232 Parallel (Synchronous) Hopfield Model 57
233 Capacities of Hopfield and Hamming Networks 59
234 Bidirectional Associative Memory 62
235 Discrete-Time Continuous-State Hopfield
Model 64
236 Stochastic Models and the Boltzmann Machine 66
2 4 Concluding Remarks 66
2 5 Problems 67
3 Competitive Learning Networks 73
3 1 Introduction 73
3 2 Basic Competitive Learning Networks 74
321 Minimal Learning Model 75
322 Training Rules Based on Normalized Weights 76
323 Training Rules for Leaky Learning 78
3 3 Adaptive Clustering Techniques: VQ and ART 78
331 Vector Quantizer (VQ) 78
332 Binary-Valued ART 79
333 Continuous-Valued ART 80
3 4 Self-Organizing Feature Map: Sensitivity to Neighborhood
and History 85
341 Self-Organizing Feature Map 85
342 Competitive Learning with History Sensitivity 87
3 5 Neocognitron: Hierarchically Structured Model 91
3 6 Concluding Remarks 95
3 7 Problems 95
Contents ix
PART III SUPERVISED MODELS
4 Decision-Based Neural Networks 99
4 1 Introduction 99
4 2 Linear Perceptron Networks 104
421 Linear Perceptron for Binary Classification 105
422 Linear Perceptron for Multiple Classification 110
4 3 Decision-Based Neural Networks 115
431 Decision-Based Learning Rule 116
432 Hierarchical DBNN Structure 118
4J3 3 Static and Temporal DBNNs 122
434 Fuzzy Decision Neural Networks 124
4 4 Applications to Signal/Image Classifications 130
441 Texture Classification 130
442 Optical Character Recognition (OCR) Application 132
443 DTW Temporal Networks for Speech Recognition 133
4 5 Concluding Remarks , 139
4 6 Problems 140
5 Approximation/Optimization Neural Networks 145
5 1 Introduction 145
5 2 Linear Approximation Networks 148
521 Delta Learning Rule: ADALINE 149
522 Kaczmarz Projection Method and Learning Rates 150
5 3 Nonlinear Multilayer Back-Propagation Networks 152
531 Back-Propagation Algorithm 154
532 Numerical Back-Propagation Methods 156
5 4 Training Versus Generalization Performance 167
541 Approximation Analysis and Training Performance 168
542 Generalization Performance 171
543 RBF Regularization Networks 175
544 Network Pruning and Growing Techniques 179
5 5 Applications of Back-Propagation Networks 184
551 Approximation Formulation: Image Compression 185
552 Optimization Formulation: Surface Reconstruction 186
553 OCON Applications: OCR, Speech, and Texture 187
x Contents
554 Control Application: Robotic Path Control 190
5 6 Concluding Remarks 192
5 7 Problems 194
PART IV TEMPORAL MODELS
6 Deterministic Temporal Neural Networks 203
6 1 Introduction 203
6 2 Linear Temporal Dynamic Models 205
621 Non-recursive Linear Predictive Filter 207
622 Recursive (IIR) Adaptive Filters 208
623 State-Space Representations 209
6 3 Nonlinear Temporal Dynamic Models 215
631 Nonrecurrent Temporal Dynamic Model (TDNN) 215
632 Recurrent Neurai Networks (RNN) 219
633 Hamiltonian Theoretical Approach to
General Learning 222
6 4 Prediction-Based Temporal Networks 224
641 Prediction-Based Independent Training Model 225
642 Compare Static and Temporal Models for
ECG Analysis 226
6 5 Concluding Remarks 233
6 6 Problems 234
7 Stochastic Temporal Networks: Hidden Markov Models 237
7 1 Introduction 237
7 2 From Markov Model to Hidden Markov Model 239
721 Markov Model 239
722 Hidden Markov Model 242
7 3 Learning Phase of Hidden Markov Models 245
731 Learning Formulas for MM 245
732 BP-Type HMM Learning Rule 247
7 4 Retrieving Phase of Hidden Markov Models 253
741 Viterbi Algorithm for Model-Scoring Problem 254
742 Pattern-Completion Problem 257
7 5 Applications to Speech, ECG, and Character Recognition 258
Contents xi
751 Application to Isolated Digit Recognition 258
752 Application to ECG Recognition 258
753 Application to Optical Character Recognition 259
7 6 Concluding Remarks 266
7 7 Problems 267
PARTV ADVANCED TOPICS
8 Principal Component Neural Networks 269
8 1 Introduction 269
8 2 From Wiener Filtering to PCA 271
821 Symmetric Principal Component Analysis (PCA) 273
822 Asymmetric Principal Component Analysis 274
8 3 Symmetric Principal Component Analysis 277
831 Extraction of the Single Principal Component 277
832 Multiple Principal Components: Lateral
Orthogonalization Network 281
8 4 BP Network for Asymmetric PCA Problems 289
841 Extraction of the Single Principal Component 289
842 Lateral Networks for Multiple Components 290
8 5 Applications to Signal/Image Processing 296
851 Rotational Compensation Applications 297
852 Data-Compression Applications: Extraction of
Innovative Components 297
853 Signal-Separation Applications: Separate
Signal from Noise 298
854 Sinusoidal Retrieval Applications: Harmonic
Spectrum Analysis 300
855 Signal-Restoration Applications: Remove or Cancel
Unwanted Noises 301
856 Subspace Classification Applications 301
8 6 Concluding Remarks 304
8 7 Problems 305
9 Stochastic Annealing Networks for Optimization 311
9 1 Introduction 311
xii Contents
9 2 Stochastic Neural Networks 313
921 Equilibrium in Stochastic Networks 313
922 Stochastic Simulated Annealing (SSA) 315
923 Mean-Field Annealing and the Continuous-Valued
Hopfield Model 317
9 3 Applications to Combinatorial Optimization and
Image Restoration 320
931 Combinatorial Optimization 321
932 Image-Restoration Model: Markov Random
Field (MRF) 325
j) 9 4 Boltzmann Machine 328
941 Training Phase 329
942 Retrieving Phases 332
9 5 Concluding Remarks 334
9 6 Problems 334
PART VI IMPLEMENTATION
10 Architecture and Implementation 337
10 1 Introduction 337
10 2 Mapping Neural Nets to Array Architectures 340
10 2 1 Mapping Design Methodology 340
10 2 2 Design for Multilayer Networks:
Retrieving Phase 345
10 2 3 Design for Multilayer Networks: Training Phase 350
10 2 4 System Design and Simulation 362
10 3 Dedicated Neural Processing Circuits 368
10 3 1 Analog Electronic Circuits 371
10 3 2 Digital (or Mostly Digital) ASIC Chips 374
10 3 3 Digital Design Based on FPGA Chips 376
10 3 4 Optical Implementation of Neural Nets 378
10 4 General-Purpose Digital Neurocomputers 380
10 4 1 Overall System Configuration 381
10 4 2 Processor Element Architecture 384
10 4 3 Parallel Array Architectures 392
Contents xiii
10 5 Concluding Remarks 405
10 6 Problems 406
Bibliography 413
Index 435
|
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| isbn | 0136123260 |
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| spelling | Kung, Sun Y. Verfasser aut Digital neural networks S. Y. Kung Englewood Cliffs, NJ Prentice-Hall 1993 XVIII, 444 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Prentice-Hall information and system sciences series Neuronales Netz (DE-588)4226127-2 gnd rswk-swf Matériel didactique Neuronales Netz (DE-588)4226127-2 s DE-604 HEBIS Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=005413657&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Kung, Sun Y. Digital neural networks Neuronales Netz (DE-588)4226127-2 gnd |
| subject_GND | (DE-588)4226127-2 |
| title | Digital neural networks |
| title_auth | Digital neural networks |
| title_exact_search | Digital neural networks |
| title_full | Digital neural networks S. Y. Kung |
| title_fullStr | Digital neural networks S. Y. Kung |
| title_full_unstemmed | Digital neural networks S. Y. Kung |
| title_short | Digital neural networks |
| title_sort | digital neural networks |
| topic | Neuronales Netz (DE-588)4226127-2 gnd |
| topic_facet | Neuronales Netz Matériel didactique |
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