Fundamentals of artificial neural networks:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Cambridge, Mass. [u.a.]
MIT Press
1995
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| Schriftenreihe: | A Bradford book
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| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XXVI, 511 S. graph. Darst. |
| ISBN: | 026208239X |
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| adam_text | FUNDAMENTALS OF ARTIFICIAL NEURAL NETWORKS MOHAMAD H. HASSOUN A BRADFORD
BOOK THE MIT PRESS CAMBRIDGE, MASSACHUSETTS LONDON, ENGLAND CONTENTS
PREFACE XIII ACKNOWLEDGMENTS XIX ABBREVIATIONS XXI SYMBOLS XXIII 1
THRESHOLD GATES 1 1.1 THRESHOLD GATES 2 1.1.1 LINEAR THRESHOLD GATES 2
1.1.2 QUADRATIC THRESHOLD GATES 7 1.1.3 POLYNOMIAL THRESHOLD GATES 8 1.2
COMPUTATIONAL CAPABILITIES OF POLYNOMIAL THRESHOLD GATES 9 1.3 GENERAL
POSITION AND THE FUNCTION COUNTING THEOREM 15 1.3.1 WEIERSTRASS S
APPROXIMATION THEOREM 15 1.3.2 POINTS IN GENERAL POSITION 16 1.3.3
FUNCTION COUNTING THEOREM 17 1.3.4 SEPARABILITY IN ^-SPACE 20 1.4
MINIMAL PTG REALIZATION OF ARBITRARY SWITCHING FUNCTIONS 21 1.5
AMBIGUITY AND GENERALIZATION 24 1.6 EXTREME POINTS 27 1.7 SUMMARY 29
PROBLEMS 30 2 COMPUTATIONAL CAPABILITIES OF ARTIFICIAL NEURAL NETWORKS
35 2.1 SOME PRELIMINARY RESULTS ON NEURAL NETWORK MAPPING CAPABILITIES
35 2.1.1 NETWORK REALIZATION OF BOOLEAN FUNCTIONS 35 2.1.2 BOUNDS ON THE
NUMBER OF FUNCTIONS REALIZABLE BY A FEEDFORWARD NETWORK OF LTG S 38 2.2
NECESSARY LOWER BOUNDS ON THE SIZE OF LTG NETWORKS 41 2.2.1 TWO LAYER
FEEDFORWARD NETWORKS 41 2.2.2 THREE LAYER FEEDFORWARD NETWORKS 44 2.2.3
GENERALLY INTERCONNECTED NETWORKS WITH NO FEEDBACK 45 2.3 APPROXIMATION
CAPABILITIES OF FEEDFORWARD NEURAL NETWORKS FOR CONTINUOUS FUNCTIONS 46
2.3.1 KOLMOGOROV S THEOREM 46 2.3.2 SINGLE-HIDDEN-LAYER NEURAL NETWORKS
ARE UNIVERSAL APPROXIMATORS 47 2.3.3 SINGLE-HIDDEN-LAYER NEURAL NETWORKS
ARE UNIVERSAL CLASSIFIERS 50 VUL CONTENTS 2.4 COMPUTATIONAL
EFFECTIVENESS OF NEURAL NETWORKS 51 2.4.1 ALGORITHMIC COMPLEXITY 51
2.4.2 COMPUTATIONAL ENERGY 52 2.5 SUMMARY 53 PROBLEMS 54 3 LEARNING
RULES 57 3.1 SUPERVISED LEARNING IN A SINGLE-UNIT SETTING 57 3.1.1 ERROR
CORRECTION RULES 58 3.1.2 OTHER GRADIENT-DESCENT-BASED LEARNING RULES 67
3.1.3 EXTENSION OF THE JU-LMS RULE TO UNITS WITH DIFFERENTIABLE
ACTIVATION FUNCTIONS: DELTA RULE 76 3.1.4 ADAPTIVE HO-KASHYAP (AHK)
LEARNING RULES 78 3.1.5 OTHER CRITERION FUNCTIONS 82 3.1.6 EXTENSION OF
GRADIENT-DESCENT-BASED LEARNING TO STOCHASTIC UNITS 87 3.2 REINFORCEMENT
LEARNING 88 3.2.1 ASSOCIATIVE REWARD-PENALTY REINFORCEMENT LEARNING RULE
89 3.3 UNSUPERVISED LEARNING 90 3.3.1 HEBBIAN LEARNING 90 3.3.2
OJA SRULE * 92 3.3.3 YUILLE ET AL. RULE 92 3.3.4 LINSKER S RULE 95 3.3.5
HEBBIAN LEARNING IN A NETWORK SETTING: PRINCIPAL-COMPONENT ANALYSIS
(PCA) 97 3.3.6 NONLINEAR PCA 101 3.4 COMPETITIVE LEARNING 103 3.4.1
SIMPLE COMPETITIVE LEARNING 103 3.4.2 VECTOR QUANTIZATION 109 3.5
SELF-ORGANIZING FEATURE MAPS: TOPOLOGY-PRESERVING COMPETITIVE LEARNING
112 3.5.1 KOHONEN S SOFM 113 3.5.2 EXAMPLES OF SOFMS 114 3.6 SUMMARY 126
PROBLEMS 134 CONTENTS IX 4 MATHEMATICAL THEORY OF NEURAL LEARNING 143
4.1 LEARNING AS A SEARCH/APPROXIMATION MECHANISM 143 4.2 MATHEMATICAL
THEORY OF LEARNING IN A SINGLE-UNIT SETTING 145 4.2.1 GENERAL LEARNING
EQUATION 146 4.2.2 ANALYSIS OF THE LEARNING EQUATION 147 4.2.3 ANALYSIS
OF SOME BASIC LEARNING RULES 148 4.3 CHARACTERIZATION OF ADDITIONAL
LEARNING RULES 152 4.3.1 SIMPLE HEBBIAN LEARNING 154 4.3.2 IMPROVED
HEBBIAN LEARNING 155 4.3.3 OJA S RULE 156 4.3.4 YUILLE ET AL. RULE 158
4.3.5 HASSOUN S RULE 161 4.4 PRINCIPAL-COMPONENT ANALYSIS (PCA) 163 4.5
THEORY OF REINFORCEMENT LEARNING 165 4.6 THEORY OF SIMPLE COMPETITIVE
LEARNING 166 4.6.1 DETERMINISTIC ANALYSIS 167 4.6.2 STOCHASTIC ANALYSIS
168 4.7 THEORY OF FEATURE MAPPING 171 4.7.1 CHARACTERIZATION OF
KOHONEN S FEATURE MAP 171 4.7.2 SELF-ORGANIZING NEURAL FIELDS 173 4.8
GENERALIZATION 180 4.8.1 GENERALIZATION CAPABILITIES OF DETERMINISTIC
NETWORKS 180 4.8.2 GENERALIZATION IN STOCHASTIC NETWORKS 185 4.9
COMPLEXITY OF LEARNING 187 4.10 SUMMARY 190 PROBLEMS 190 5 ADAPTIVE
MULTILAYER NEURAL NETWORKS I 197 5.1 LEARNING RULE FOR MULTILAYER
FEEDFORWARD NEURAL NETWORKS 197 5.1.1 ERROR BACKPROPAGATION LEARNING
RULE 199 5.1.2 GLOBAL-DESCENT-BASED ERROR BACKPROPAGATION 206 5.2
BACKPROP ENHANCEMENTS AND VARIATIONS 210 5.2.1 WEIGHTS INITIALIZATION
210 5.2.2 LEARNING RATE 211 5.2.3 MOMENTUM 213 X CONTENTS 5.2.4
ACTIVATION FUNCTION 219 5.2.5 WEIGHT DECAY, WEIGHT ELIMINATION, AND UNIT
ELIMINATION 221 5.2.6 CROSS-VALIDATION 226 5.2.7 CRITERION FUNCTIONS 230
5.3 APPLICATIONS 234 5.3.1 NETTALK 234 5.3.2 GLOVE-TALK 236 5.3.3
HANDWRITTEN ZIP CODE RECOGNITION 240 5.3.4 ALVINN: A TRAINABLE
AUTONOMOUS LAND VEHICLE 244 5.3.5 MEDICAL DIAGNOSIS EXPERT NET 246 5.3.6
IMAGE COMPRESSION AND DIMENSIONALITY REDUCTION 247 5.4 EXTENSIONS OF
BACKPROP FOR TEMPORAL LEARNING 253 5.4.1 TIME-DELAY NEURAL NETWORKS 254
5.4.2 BACKPROPAGATION THROUGH TIME 259 5.4.3 RECURRENT BACKPROPAGATION
267 5.4.4 TIME-DEPENDENT RECURRENT BACKPROPAGATION 271 5.4.5 REAL-TIME
RECURRENT LEARNING 274 5.5 SUMMARY 275 PROBLEMS 276 6 ADAPTIVE
MULTILAYER NEURAL NETWORKS II 285 6.1 RADIAL BASIS FUNCTION (RBF)
NETWORKS 285 6.1.1 RBF NETWORKS VERSUS BACKPROP NETWORKS 294 6.1.2 RBF
NETWORK VARIATIONS 296 6.2 CEREBELLER MODEL ARTICULATION CONTROLLER
(CMAC) 301 6.2.1 CMAC RELATION TO ROSENBLATT S PERCEPTRON AND OTHER
MODELS 304 6.3 UNIT-ALLOCATING ADAPTIVE NETWORKS 310 6.3.1
HYPERSPHERICAL CLASSIFIERS 311 6.3.2 CASCADE-CORRELATION NETWORK 318 6.4
CLUSTERING NETWORKS 322 6.4.1 ADAPTIVE RESONANCE THEORY (ART) NETWORKS
323 6.4.2 AUTOASSOCIATIVE CLUSTERING NETWORK 328 6.5 SUMMARY 337
PROBLEMS 339 CONTENTS 7 ASSOCIATIVE NEURAL MEMORIES 345 7.1 BASIC
ASSOCIATIVE NEURAL MEMORY MODELS 345 7.1.1 SIMPLE ASSOCIATIVE MEMORIES
AND THEIR ASSOCIATED RECORDING RECIPES 346 7.1.2 DYNAMIC ASSOCIATIVE
MEMORIES (DAMS) 353 7.2 DAM CAPACITY AND RETRIEVAL DYNAMICS 363 7.2.1
CORRELATION DAMS 363 7.2.2 PROJECTION DAMS 369 7.3 CHARACTERISTICS OF
HIGH-PERFORMANCE DAMS 374 7.4 OTHER DAM MODELS 375 7.4.1
BRAIN-STATE-IN-A-BOX (BSB) DAM 375 7.4.2 NONMONOTONIC ACTIVATIONS DAM
381 7.4.3 HYSTERETIC ACTIVATIONS DAM 386 7.4.4 EXPONENTIAL-CAPACITY DAM
389 7.4.5 SEQUENCE-GENERATOR DAM 391 7.4.6 HETEROASSOCIATIVE DAM 392 7.5
THE DAM AS A GRADIENT NET AND ITS APPLICATION TO COMBINATORIAL
OPTIMIZATION 394 7.6 SUMMARY 400 PROBLEMS 401 8 GLOBAL SEARCH METHODS
FOR NEURAL NETWORKS 417 8.1 LOCAL VERSUS GLOBAL SEARCH 417 8.1.1 A
GRADIENT DESCENT/ASCENT SEARCH STRATEGY 419 8.1.2 STOCHASTIC GRADIENT
SEARCH: GLOBAL SEARCH VIA DIFFUSION 421 8.2 SIMULATED ANNEALING-BASED
GLOBAL SEARCH 424 8.3 SIMULATED ANNEALING FOR STOCHASTIC NEURAL NETWORKS
428 8.3.1 GLOBAL CONVERGENCE IN A STOCHASTIC RECURRENT NEURAL NET: THE
BOLTZMANN MACHINE 429 8.3.2 LEARNING IN BOLTZMANN MACHINES 431 8.4
MEAN-FIELD ANNEALING AND DETERMINISTIC BOLTZMANN MACHINES 436 8.4.1
MEAN-FIELD RETRIEVAL 437 8.4.2 MEAN-FIELD LEARNING 438 8.5 GENETIC
ALGORITHMS IN NEURAL NETWORK OPTIMIZATION 439 8.5.1 FUNDAMENTALS OF
GENETIC ALGORITHMS 439 8.5.2 APPLICATION OF GENETIC ALGORITHMS TO NEURAL
NETWORKS 452 XII CONTENTS 8.6 GENETIC ALGORITHM-ASSISTED SUPERVISED
LEARNING 454 8.6.1 HYBRID GA/GRADIENT-DESCENT METHOD FOR FEEDFORWARD
MULTILAYER NET TRAINING 455 8.6.2 SIMULATIONS 458 8.7 SUMMARY 462
PROBLEMS 463 REFERENCES 469 INDEX 501
|
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| author_facet | Hassoun, Mohamad H. |
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| dewey-search | 006.3 20 |
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| discipline | Informatik |
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| indexdate | 2024-07-09T17:50:22Z |
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| language | English |
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| series2 | A Bradford book |
| spelling | Hassoun, Mohamad H. Verfasser aut Fundamentals of artificial neural networks Mohamad H. Hassoun Cambridge, Mass. [u.a.] MIT Press 1995 XXVI, 511 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier A Bradford book Intelligence artificielle Intelligence artificielle ram Réseau neuronal (informatique) Réseaux neuronaux (Informatique) Réseaux neuronaux (informatique) ram algorithme optimisation inriac apprentissage machine inriac intelligence artificielle inriac mémoire associative inriac réseau multicouche inriac réseau neuronal inriac réseau à seuil inriac Künstliche Intelligenz Neural networks (Computer science) Artificial intelligence Neuronales Netz (DE-588)4226127-2 gnd rswk-swf Künstliche Intelligenz (DE-588)4033447-8 gnd rswk-swf Neuronales Netz (DE-588)4226127-2 s DE-604 Künstliche Intelligenz (DE-588)4033447-8 s GBV Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=006863420&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Hassoun, Mohamad H. Fundamentals of artificial neural networks Intelligence artificielle Intelligence artificielle ram Réseau neuronal (informatique) Réseaux neuronaux (Informatique) Réseaux neuronaux (informatique) ram algorithme optimisation inriac apprentissage machine inriac intelligence artificielle inriac mémoire associative inriac réseau multicouche inriac réseau neuronal inriac réseau à seuil inriac Künstliche Intelligenz Neural networks (Computer science) Artificial intelligence Neuronales Netz (DE-588)4226127-2 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd |
| subject_GND | (DE-588)4226127-2 (DE-588)4033447-8 |
| title | Fundamentals of artificial neural networks |
| title_auth | Fundamentals of artificial neural networks |
| title_exact_search | Fundamentals of artificial neural networks |
| title_full | Fundamentals of artificial neural networks Mohamad H. Hassoun |
| title_fullStr | Fundamentals of artificial neural networks Mohamad H. Hassoun |
| title_full_unstemmed | Fundamentals of artificial neural networks Mohamad H. Hassoun |
| title_short | Fundamentals of artificial neural networks |
| title_sort | fundamentals of artificial neural networks |
| topic | Intelligence artificielle Intelligence artificielle ram Réseau neuronal (informatique) Réseaux neuronaux (Informatique) Réseaux neuronaux (informatique) ram algorithme optimisation inriac apprentissage machine inriac intelligence artificielle inriac mémoire associative inriac réseau multicouche inriac réseau neuronal inriac réseau à seuil inriac Künstliche Intelligenz Neural networks (Computer science) Artificial intelligence Neuronales Netz (DE-588)4226127-2 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd |
| topic_facet | Intelligence artificielle Réseau neuronal (informatique) Réseaux neuronaux (Informatique) Réseaux neuronaux (informatique) algorithme optimisation apprentissage machine intelligence artificielle mémoire associative réseau multicouche réseau neuronal réseau à seuil Künstliche Intelligenz Neural networks (Computer science) Artificial intelligence Neuronales Netz |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=006863420&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT hassounmohamadh fundamentalsofartificialneuralnetworks |