Process neural networks: theory and applications
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Hangzhou
Zhejiang Univ. Press
2009
Heidelberg [u.a.] Springer |
| Schriftenreihe: | Advanced topics in science and technology in China
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Literaturangaben |
| Beschreibung: | XII, 240 S. graph. Darst. 24 cm |
| ISBN: | 9783540737612 9787308055116 3540737618 |
Internformat
MARC
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| 100 | 1 | |a He, Xingui |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Process neural networks |b theory and applications |c Xingui He ; Shaohua Xu |
| 264 | 1 | |a Hangzhou |b Zhejiang Univ. Press |c 2009 | |
| 264 | 1 | |a Heidelberg [u.a.] |b Springer | |
| 300 | |a XII, 240 S. |b graph. Darst. |c 24 cm | ||
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Datensatz im Suchindex
| _version_ | 1804140931963682816 |
|---|---|
| adam_text | CONTENTS 1 INTRODUCTION 1 1.1 DEVELOPMENT OF ARTIFICIAL INTELLIGENCE 1
1.2 CHARACTERISTICS OF ARTIFICIAL INTELLIGENT SYSTEM 5 1.3 COMPUTATIONAL
INTELLIGENCE 9 1.3.1 FUZZY COMPUTING 9 1.3.2 NEURAL COMPUTING 12 1.3.3
EVOLUTIONARY COMPUTING 12 1.3.4 COMBINATION OF THE THREE BRANCHES 15 1.4
PROCESS NEURAL NETWORKS 16 REFERENCES 17 2 ARTIFICIAL NEURAL NETWORKS 20
2.1 BIOLOGICAL NEURON 21 2.2 MATHEMATICAL MODEL OF A NEURON 22 2.3
FEEDFORWARD/FEEDBACK NEURAL NETWORKS 23 2.3.1 FEEDFORWARD/FEEDBACK
NEURAL NETWORK MODEL 23 2.3.2 FUNCTION APPROXIMATION CAPABILITY OF
FEEDFORWARD NEURAL NETWORKS 25 2.3.3 COMPUTING CAPABILITY OF FEEDFORWARD
NEURAL NETWORKS 27 2.3.4 LEARNING ALGORITHM FOR FEEDFORWARD NEURAL
NETWORKS 28 2.3.5 GENERALIZATION PROBLEM FOR FEEDFORWARD NEURAL NETWORKS
28 2.3.6 APPLICATIONS OF FEEDFORWARD NEURAL NETWORKS 30 2.4 FUZZY NEURAL
NETWORKS 32 2.4.1 FUZZY NEURONS 32 2.4.2 FUZZY NEURAL NETWORKS 33
BIBLIOGRAFISCHE INFORMATIONEN HTTP://D-NB.INFO/984687416 DIGITALISIERT
DURCH VIII CONTENTS 2.5 NONLINEAR AGGREGATION ARTIFICIAL NEURAL NETWORKS
35 2.5.1 STRUCTURAL FORMULA AGGREGATION ARTIFICIAL NEURAL NETWORKS 35
2.5.2 MAXIMUM (OR MINIMUM) AGGREGATION ARTIFICIAL NEURAL NETWORKS 35
2.5.3 OTHER NONLINEAR AGGREGATION ARTIFICIAL NEURAL NETWORKS 36 2.6
SPATIO-TEMPORAL AGGREGATION AND PROCESS NEURAL NETWORKS 37 2.7
CLASSIFICATION OF ARTIFICIAL NEURAL NETWORKS 39 REFERENCES 40 3 PROCESS
NEURONS 43 3.1 REVELATION OF BIOLOGICAL NEURONS 43 3.2 DEFINITION OF
PROCESS NEURONS 44 3.3 PROCESS NEURONS AND FUNCTIONALS 47 3.4 FUZZY
PROCESS NEURONS 48 3.4.1 PROCESS NEURON FUZZINESS 49 3.4.2 FUZZY PROCESS
NEURONS CONSTRUCTED USING FUZZY WEIGHTED REASONING RULE 50 3.5 PROCESS
NEURONS AND COMPOUND FUNCTIONS 51 REFERENCES 52 4 FEEDFORWARD PROCESS
NEURAL NETWORKS 53 4.1 SIMPLE MODEL OF A FEEDFORWARD PROCESS NEURAL
NETWORK 53 4.2 A GENERAL MODEL OF A FEEDFORWARD PROCESS NEURAL NETWORK
55 4.3 A PROCESS NEURAL NETWORK MODEL BASED ON WEIGHT FUNCTION BASIS
EXPANSION 56 4.4 BASIC THEOREMS OF FEEDFORWARD PROCESS NEURAL NETWORKS
58 4.4.1 EXISTENCE OF SOLUTIONS 59 4.4.2 CONTINUITY 62 4.4.3 FUNCTIONAL
APPROXIMATION PROPERTY 64 4.4.4 COMPUTING CAPABILITY 67 4.5 STRUCTURAL
FORMULA FEEDFORWARD PROCESS NEURAL NETWORKS 67 4.5.1 STRUCTURAL FORMULA
PROCESS NEURONS 68 4.5.2 STRUCTURAL FORMULA PROCESS NEURAL NETWORK MODEL
69 4. CONTENTS IX 4.6.2 CONTINUITY AND APPROXIMATION CAPABILITY OF THE
MODEL 73 4.7 CONTINUOUS PROCESS NEURAL NETWORKS 75 4.7.1 CONTINUOUS
PROCESS NEURONS 76 4.7.2 CONTINUOUS PROCESS NEURAL NETWORK MODEL 77
4.7.3 CONTINUITY, APPROXIMATION CAPABILITY, AND COMPUTING CAPABILITY OF
THE MODEL 78 4.8 FUNCTIONAL NEURAL NETWORK 83 4.8.1 FUNCTIONAL NEURON 84
4.8.2 FEEDFORWARD FUNCTIONAL NEURAL NETWORK MODEL 85 4.9 EPILOGUE 86
REFERENCES 87 5 LEARNING ALGORITHMS FOR PROCESS NEURAL NETWORKS 88 5.1
LEARNING ALGORITHMS BASED ON THE GRADIENT DESCENT METHOD AND NEWTON
DESCENT METHOD 89 5.1.1 A GENERAL LEARNING ALGORITHM BASED ON GRADIENT
DESCENT 89 5.1.2 LEARNING ALGORITHM BASED ON GRADIENT-NEWTON COMBINATION
91 5.1.3 LEARNING ALGORITHM BASED ON THE NEWTON DESCENT METHOD 93 5.2
LEARNING ALGORITHM BASED ON ORTHOGONAL BASIS EXPANSION 93 5.2.1
ORTHOGONAL BASIS EXPANSION OF INPUT FUNCTIONS 94 5.2.2 LEARNING
ALGORITHM DERIVATION 95 5.2.3 ALGORITHM DESCRIPTION AND COMPLEXITY
ANALYSIS 96 5.3 LEARNING ALGORITHM BASED ON THE FOURIER FUNCTION
TRANSFORMATION 97 5.3.1 FOURIER ORTHOGONAL BASIS EXPANSION OF THE
FUNCTION IN L 2 [0,2N] .... 97 5.3.2 LEARNING ALGORITHM DERIVATION 99
5.4 LEARNING ALGORITHM BASED ON THE WALSH FUNCTION TRANSFORMATION 101
5.4.1 LEARNING ALGORITHM BASED ON DISCRETE WALSH FUNCTION TRANSFORMATION
101 5.4. X CONTENTS 5.6 LEARNING ALGORITHM BASED ON RATIONAL SQUARE
APPROXIMATION AND OPTIMAL PIECEWISE APPROXIMATION 112 5.6.1 LEARNING
ALGORITHM BASED ON RATIONAL SQUARE APPROXIMATION ... 112 5.6.2 LEARNING
ALGORITHM BASED ON OPTIMAL PIECEWISE APPROXIMATION 119 5.7 EPILOGUE 126
REFERENCES 126 6 FEEDBACK PROCESS NEURAL NETWORKS 128 6.1 A THREE-LAYER
FEEDBACK PROCESS NEURAL NETWORK 129 6.1.1 NETWORK STRUCTURE 129 6.1.2
LEARNING ALGORITHM 130 6.1.3 STABILITY ANALYSIS 132 6.2 OTHER FEEDBACK
PROCESS NEURAL NETWORKS 135 6.2.1 FEEDBACK PROCESS NEURAL NETWORK WITH
TIME-VARYING FUNCTIONS AS INPUTS AND OUTPUTS 135 6.2.2 FEEDBACK PROCESS
NEURAL NETWORK FOR PATTERN CLASSIFICATION 136 6.2.3 FEEDBACK PROCESS
NEURAL NETWORK FOR ASSOCIATIVE MEMORY STORAGE 137 6.3 APPLICATION
EXAMPLES 138 REFERENCES 142 7 MULTI-AGGREGATION PROCESS NEURAL NETWORKS
143 7.1 MULTI-AGGREGATION PROCESS NEURON 143 7.2 MULTI-AGGREGATION
PROCESS NEURAL NETWORK MODEL 145 7.2.1 A GENERAL MODEL OF
MULTI-AGGREGATION PROCESS NEURAL NETWORK 145 7.2.2 MULTI-AGGREGATION
PROCESS NEURAL NETWORK MODEL WITH MULTIVARIATE PROCESS FUNCTIONS AS
INPUTS AND OUTPUTS 147 7.3 LEARNING ALGORITHM 148 7.3.1 LEARNING
ALGORITHM OF GENERAL MODELS OF MULTI-AGGREGATION PROCESS NEURAL NETWORKS
148 7.3.2 LEARNING ALGORITHM OF MULTI-AGGREGATION PROCESS NEURAL NETWORK
9.2 APPLICATION IN NONLINEAR SYSTEM IDENTIFICATION 198 CONTENTS XI
REFERENCES 160 8 DESIGN AND CONSTRUCTION OF PROCESS NEURAL NETWORKS 161
8.1 PROCESS NEURAL NETWORKS WITH DOUBLE HIDDEN LAYERS 161 8.1.1 NETWORK
STRUCTURE 162 8.1.2 LEARNING ALGORITHM 163 8.1.3 APPLICATION EXAMPLES
165 8.2 DISCRETE PROCESS NEURAL NETWORK 166 8.2.1 DISCRETE PROCESS
NEURON 167 8.2.2 DISCRETE PROCESS NEURAL NETWORK 168 8.2.3 LEARNING
ALGORITHM 169 8.2.4 APPLICATION EXAMPLES 170 8.3 CASCADE PROCESS NEURAL
NETWORK 172 8.3.1 NETWORK STRUCTURE 173 8.3.2 LEARNING ALGORITHM 175
8.3.3 APPLICATION EXAMPLES 176 8.4 SELF-ORGANIZING PROCESS NEURAL
NETWORK 178 8.4.1 NETWORK STRUCTURE 178 8.4.2 LEARNING ALGORITHM 179
8.4.3 APPLICATION EXAMPLES 182 8.5 COUNTER PROPAGATION PROCESS NEURAL
NETWORK 184 8.5.1 NETWORK STRUCTURE 185 8.5.2 LEARNING ALGORITHM 185
8.5.3 DETERMINATION OF THE NUMBER OF PATTERN CLASSIFICATIONS 186 8.5.4
APPLICATION EXAMPLES 187 8.6 RADIAL-BASIS FUNCTION PROCESS NEURAL
NETWORK 188 8.6.1 RADIAL-BASIS PROCESS NEURON 188 8.6.2 NETWORK
STRUCTURE 189 8.6.3 LEARNING ALGORITHM 190 8.6.4 APPLICATION EXAMPLES
192 8.7 EPILOGUE 193 REFERENCES 193 9 APPLICATION OF PROCESS NEURAL
NETWORKS 195 9.1 APPLICATION IN PROCESS MODELING 195 INDEX 238 XII
CONTENTS 9.2.1 THE PRINCIPLE OF NONLINEAR SYSTEM IDENTIFICATION 199
9.2.2 THE PROCESS NEURAL NETWORK FOR SYSTEM IDENTIFICATION 200 9.2.3
NONLINEAR SYSTEM IDENTIFICATION PROCESS 201 9.3 APPLICATION IN PROCESS
CONTROL 203 9.3.1 PROCESS CONTROL OF NONLINEAR SYSTEM 204 9.3.2 DESIGN
AND SOLVING OF PROCESS CONTROLLER 204 9.3.3 SIMULATION EXPERIMENT 208
9.4 APPLICATION IN CLUSTERING AND CLASSIFICATION 210 9.5 APPLICATION IN
PROCESS OPTIMIZATION 215 9.6 APPLICATION IN FORECAST AND PREDICTION 216
9.7 APPLICATION IN EVALUATION AND DECISION 224 9.8 APPLICATION IN MACRO
CONTROL 226 9.9 OTHER APPLICATIONS 227 REFERENCES 231 POSTSCRIPT 233
|
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| author | He, Xingui Xu, Shaohua |
| author_facet | He, Xingui Xu, Shaohua |
| author_role | aut aut |
| author_sort | He, Xingui |
| author_variant | x h xh s x sx |
| building | Verbundindex |
| bvnumber | BV035943609 |
| classification_rvk | ST 301 |
| ctrlnum | (OCoLC)845135519 (DE-599)DNB984687416 |
| dewey-full | 006.32 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 006 - Special computer methods |
| dewey-raw | 006.32 |
| dewey-search | 006.32 |
| dewey-sort | 16.32 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Maschinenbau / Maschinenwesen Informatik |
| format | Book |
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| id | DE-604.BV035943609 |
| illustrated | Illustrated |
| indexdate | 2024-07-09T22:07:49Z |
| institution | BVB |
| isbn | 9783540737612 9787308055116 3540737618 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-018800807 |
| oclc_num | 845135519 |
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| physical | XII, 240 S. graph. Darst. 24 cm |
| publishDate | 2009 |
| publishDateSearch | 2009 |
| publishDateSort | 2009 |
| publisher | Zhejiang Univ. Press Springer |
| record_format | marc |
| series2 | Advanced topics in science and technology in China |
| spelling | He, Xingui Verfasser aut Process neural networks theory and applications Xingui He ; Shaohua Xu Hangzhou Zhejiang Univ. Press 2009 Heidelberg [u.a.] Springer XII, 240 S. graph. Darst. 24 cm txt rdacontent n rdamedia nc rdacarrier Advanced topics in science and technology in China Literaturangaben Systemidentifikation (DE-588)4121753-6 gnd rswk-swf Prozessregelung (DE-588)4222919-4 gnd rswk-swf Neuronales Netz (DE-588)4226127-2 gnd rswk-swf Neuronales Netz (DE-588)4226127-2 s DE-604 Systemidentifikation (DE-588)4121753-6 s Prozessregelung (DE-588)4222919-4 s Xu, Shaohua Verfasser aut DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=018800807&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | He, Xingui Xu, Shaohua Process neural networks theory and applications Systemidentifikation (DE-588)4121753-6 gnd Prozessregelung (DE-588)4222919-4 gnd Neuronales Netz (DE-588)4226127-2 gnd |
| subject_GND | (DE-588)4121753-6 (DE-588)4222919-4 (DE-588)4226127-2 |
| title | Process neural networks theory and applications |
| title_auth | Process neural networks theory and applications |
| title_exact_search | Process neural networks theory and applications |
| title_full | Process neural networks theory and applications Xingui He ; Shaohua Xu |
| title_fullStr | Process neural networks theory and applications Xingui He ; Shaohua Xu |
| title_full_unstemmed | Process neural networks theory and applications Xingui He ; Shaohua Xu |
| title_short | Process neural networks |
| title_sort | process neural networks theory and applications |
| title_sub | theory and applications |
| topic | Systemidentifikation (DE-588)4121753-6 gnd Prozessregelung (DE-588)4222919-4 gnd Neuronales Netz (DE-588)4226127-2 gnd |
| topic_facet | Systemidentifikation Prozessregelung Neuronales Netz |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=018800807&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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