Complex valued neural networks:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Berlin [u.a.]
Springer
2012
|
| Ausgabe: | 2. ed. |
| Schriftenreihe: | Studies in computational intelligence
400 |
| Schlagworte: | |
| Online-Zugang: | Inhaltstext Inhaltsverzeichnis |
| Beschreibung: | Literaturangaben |
| Beschreibung: | XVII, 197 S. Ill., graph. Darst. |
| ISBN: | 9783642276316 3642276318 9783642435799 |
Internformat
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Datensatz im Suchindex
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| adam_text |
IMAGE 1
C O N T E N T S
PART I B A S I C IDEAS A N D FUNDAMENTALS: W H Y A R E C O M P L E X - V
A L U E D NEURAL N E T W O R K S I N E V I T A B L E ?
1 C O M P L E X - V A L U E D N E U R A L N E T W O R K S FERTILIZE
ELECTRONICS . . . 3
1.1 IMITATE THE BRAIN, AND SURPASS THE BRAIN 3
1.2 CREATE A "SUPERBRAIN" BY ENRICHMENT OF THE INFORMATION
REPRESENTATION 4
1.3 APPLICATION FIELDS EXPAND RAPIDLY AND STEADILY 6
1.4 BOOK ORGANIZATION 8
2 N E U R A L NETWORKS: T H E CHARACTERISTIC V I E W P O I N T S 9
2.1 BRAIN, ARTIFICIAL BRAIN, ARTIFICIAL INTELLIGENCE (AI), AND NEURAL
NETWORKS 9
2.2 PHYSICALITY OF BRAIN FUNCTIONS 12
2.3 NEURAL NETWORKS: GENERAL FEATURES 13
3 C O M P L E X - V A L U E D N E U R A L NETWORKS: D I S T I N C T I V
E FEATURES . . 17 3.1 W H A T IS A COMPLEX NUMBER? 17
3.1.1 GEOMETRIC AND INTUITIVE DEFINITION 17
3.1.2 DEFINITION AS ORDERED PAIR OF REAL NUMBERS 18
3.1.3 REAL 2 X 2 MATRIX REPRESENTATION 19
3.2 COMPARISON OF COMPLEX- AND REAL-VALUED FEEDFORWARD NEURAL NETWORKS
20
3.2.1 FUNCTION OF COMPLEX-VALUED SYNAPSE AND NETWORK OPERATION 20
3.2.2 CIRCULARITY AND WIDELY-LINEAR SYSTEMS 23
3.2.3 NONLINEARITY T H A T ENHANCES THE FEATURES OF COMPLEX-VALUED
NETWORKS 24
3.3 ACTIVATION FUNCTIONS IN NEURONS 26
3.3.1 NONLINEAR ACTIVATION FUNCTIONS IN REAL-VALUED NEURAL NETWORKS 26
HTTP://D-NB.INFO/1018095152
IMAGE 2
XIV CONTENTS
3.3.2 PROBLEM CONCERNING ACTIVATION FUNCTIONS IN COMPLEX-VALUED NEURAL
NETWORKS 28
3.3.3 CONSTRUCTION OF CVNNS WITH PARTIAL DERIVATIVES IN COMPLEX DOMAIN
28
3.3.4 REAL-IMAGINARY-TYPE ACTIVATION FUNCTION 31
3.3.5 AMPLITUDE-PHASE-TYPE ACTIVATION FUNCTION 32
3.4 METRIC IN COMPLEX DOMAIN 34
3.4.1 IMPORTANCE OF METRIC: AN EXAMPLE IN COMPLEX-VALUED SELF-ORGANIZING
MAP 34
3.4.2 EUCLIDEAN METRIC 34
3.4.3 COMPLEX-VALUED INNER-PRODUCT METRIC 36
3.4.4 COMPARISON BETWEEN COMPLEX-VALUED INNER PRODUCT AND EUCLIDEAN
DISTANCE 36
- 3.4.5 METRIC IN CORRELATION LEARNING 37
3.5 W H A T IS THE SENSE OF COMPLEX-VALUED INFORMATION AND ITS
PROCESSING? 38
3.6 IN W H A T FIELDS ARE CVNNS EFFECTIVE? 40
3.6.1 ELECTROMAGNETIC AND OPTICAL WAVES, ELECTRICAL SIGNALS IN ANALOG
AND DIGITAL CIRCUITS 40
3.6.2 ELECTRON WAVE 43
3.6.3 SUPERCONDUCTORS 45
3.6.4 QUANTUM COMPUTATION 45
3.6.5 SONIC AND ULTRASONIC WAVES 45
3.6.6 COMPATIBILITY OF CONTROLLABILITY AND ADAPTABILITY 46 3.6.7
PERIODIC TOPOLOGY AND METRIC 46
3.6.8 DIRECT USE OF POLAR COORDINATES 48
3.6.9 HIGH STABILITY IN RECURRENT DYNAMICS 48
3.6.10 PRESERVATION OF RELATIVE DIRECTIONS AND SEGMENTATION BOUNDARIES
IN TWO-DIMENSIONAL INFORMATION TRANSFORM 49
3.6.11 CHAOS AND FRACTALS IN COMPLEX DOMAIN 49
3.6.12 QUATERNION AND OTHER HIGHER-ORDER COMPLEX NUMBERS 49
3.7 INVESTIGATIONS IN COMPLEX-VALUED NEURAL NETWORKS 50
3.7.1 HISTORY 50
3.7.2 RECENT PROGRESS 53
4 C O N S T R U C T I O N S A N D D Y N A M I C S O F N E U R A L N E T
W O R K S 57
4.1 PROCESSING, LEARNING, AND SELF-ORGANIZATION 57
4.1.1 PULSE-DENSITY SIGNAL REPRESENTATION 57
4.1.2 NEURAL DYNAMICS 59
4.1.3 TASK PROCESSING 59
4.1.4 LEARNING AND SELF-ORGANIZATION 60
4.1.5 CHANGES IN CONNECTION WEIGHTS 60
4.2 HEBBIAN RULE 60
IMAGE 3
CONTENTS XV
4.3 ASSOCIATIVE MEMORY 63
4.3.1 FUNCTION: MEMORY AND RECALL OF P A T T E R N INFORMATION 63
4.3.2 NETWORK CONSTRUCTION AND PROCESSING DYNAMICS 63
4.3.3 ENERGY FUNCTION 67
4.3.4 USE OF GENERALIZED INVERSE MATRIX 69
4.3.5 WEIGHT LEARNING BY SEQUENTIAL CORRELATION LEARNING . . 69 4.3.6
COMPLEX-VALUED ASSOCIATIVE MEMORY 70
4.3.7 AMPLITUDE-PHASE EXPRESSION OF HEBBIAN RULE 72
4.3.8 LIGHTWAVE NEURAL NETWORKS AND CARRIER-FREQUENCY-DEPENDENT LEARNING
73
4.4 FUNCTION APPROXIMATION 76
4.4.1 FUNCTION: GENERATION OF DESIRABLE OUTPUTS FOR GIVEN INPUTS 76
4.4.2 NETWORK CONSTRUCTION AND PROCESSING DYNAMICS 76 4.4.3 LEARNING BY
STEEPEST DESCENT METHOD 78
4.4.4 BACKPROPAGATION LEARNING 79
4.4.5 LEARNING BY COMPLEX-VALUED STEEPEST DESCENT METHOD 81
4.4.6 FUNCTION APPROXIMATION BY USE OF COMPLEX-VALUED HEBBIAN RULE 86
4.4.7 BACKPROPAGATION LEARNING BY BACKWARD PROPAGATION OF TEACHER
SIGNALS INSTEAD OF ERRORS 87
4.5 ADAPTIVE CLUSTERING AND VISUALIZATION OF MULTIDIMENSIONAL
INFORMATION 90
4.5.1 FUNCTION: VECTOR QUANTIZATION AND VISUALIZATION 90
4.5.2 NETWORK CONSTRUCTION, PROCESSING, AND SELF-ORGANIZATION 91
4.5.3 COMPLEX-VALUED SELF-ORGANIZING MAP: CSOM 94
4.6 MARKOV RANDOM FIELD ESTIMATION 94
4.6.1 FUNCTION: SIGNAL ESTIMATION FROM NEIGHBORS 94
4.6.2 NETWORK CONSTRUCTION AND PROCESSING DYNAMICS 95
4.6.3 LEARNING CORRELATIONS BETWEEN SIGNALS A T A PIXEL AND ITS
NEIGHBORS 96
4.7 PRINCIPAL COMPONENT ANALYSIS 97
4.7.1 FUNCTION: EXTRACTION OF PRINCIPAL INFORMATION IN STATISTICAL D A T
A 97
4.7.2 NETWORK CONSTRUCTION AND DYNAMICS OF TASK PROCESSING AND
SELF-ORGANIZATION 97
4.8 INDEPENDENT COMPONENT ANALYSIS 99
IMAGE 4
XVI CONTENTS
P A R T II APPLICATIONS: H O W W I D E A R E T H E A P P L I C A T I O N
F I E L D S ?
5 LAND-SURFACE CLASSIFICATION W I T H U N E V E N N E S S A N D
REFLECTANCE TAKEN INTO CONSIDERATION 103
5.1 INTERFEROMETRIC RADAR 103
5.2 CMRF MODEL 104
5.3 CMRF MODEL AND COMPLEX-VALUED HEBBIAN LEARNING RULE. . . 107 5.4
CONSTRUCTION OF CSOM IMAGE CLASSIFICATION SYSTEM 108
5.5 GENERATION OF LAND-SURFACE CLASSIFICATION MAP 110
5.6 SUMMARY I L L
6 A D A P T I V E R A D A R S Y S T E M T O VISUALIZE A N T I P E R S O
N N E L
P L A S T I C LANDMINES 113
6.1 GROUND PENETRATING RADARS 113
6.2 CONSTRUCTION OF CSOM PLASTIC LANDMINE VISUALIZATION SYSTEM DEALING
WITH FREQUENCY- AND SPACE-DOMAIN TEXTURE 114
6.3 ADAPTIVE SIGNAL PROCESSING IN CSOM 115
6.3.1 FEATURE VECTOR EXTRACTION BY PAYING ATTENTION T O FREQUENCY DOMAIN
INFORMATION 115
6.3.2 DYNAMICS OF CSOM CLASSIFICATION 117
6.4 VISUALIZATION OF ANTIPERSONNEL PLASTIC LANDMINES 118
6.4.1 MEASUREMENT PARAMETERS 118
6.4.2 RESULTS OF OBSERVATION AND CLASSIFICATION 120
6.4.3 PERFORMANCE EVALUATION BY VISUALIZATION RATE 121
6.5 SUMMARY 121
7 R E M O V A L O F P H A S E SINGULAR P O I N T S T O C R E A T E D I G
I T A L
ELEVATION M A P 123
7.1 PHASE UNWRAPPING 123
7.2 NOISE REDUCTION WITH A COMPLEX-VALUED CELLULAR NEURAL NETWORK 125
7.3 SYSTEM CONSTRUCTION 127
7.4 DYNAMICS OF SINGULAR-POINT REDUCTION 128
7.5 DEM QUALITY AND CALCULATION COST 129
7.6 SUMMARY 131
8 LIGHTWAVE A S S O C I A T I V E M E M O R Y T H A T M E M O R I Z E S
A N D RECALLS INFORMATION D E P E N D I N G O N OPTICAL-CARRIER
FREQUENCY 133
8.1 UTILIZATION OF WIDE FREQUENCY BANDWIDTH IN OPTICAL NEURAL NETWORKS
133
8.2 OPTICAL-CARRIER-FREQUENCY DEPENDENT ASSOCIATIVE MEMORY: THE DYNAMICS
136
IMAGE 5
CONTENTS XVII
8.2.1 RECALLING PROCESS 136
8.2.2 MEMORIZING PROCESS 136
8.3 OPTICAL SETUP 137
8.4 FREQUENCY-DEPENDENT LEARNING 138
8.5 FREQUENCY-DEPENDENT RECALL EXPERIMENT 141
8.6 SUMMARY 142
9 A D A P T I V E O P T I C A L - P H A S E EQUALIZER 143
9.1 SYSTEM CONSTRUCTION 143
9.2 OPTICAL SETUP 144
9.3 DYNAMICS OF OUTPUT PHASE-VALUE LEARNING 146
9.4 PERFORMANCE OF PHASE EQUALIZATION 147
9.5 SUMMARY 149
10 D E V E L O P M E N T A L LEARNING W I T H B E H A V I O R A L - M O
D E T U N I N G B Y CARRIER-FREQUENCY M O D U L A T I O N 151
10.1 DEVELOPMENT, CONTEXT DEPENDENCE, VOLITION, AND DEVELOPMENTAL
LEARNING 151
10.2 NEURAL CONSTRUCTION AND HUMAN-BICYCLE MODEL 153
10.3 DEVELOPMENTAL LEARNING IN BICYCLE RIDING 156
10.3.1 TASK 1: RIDE AS LONG AS POSSIBLE 157
10.3.2 TASK 2: RIDE AS FAR AS POSSIBLE 160
10.3.3 COMPARATIVE EXPERIMENT: DIRECT FML IN TASK 2 161 10.3.4
COMPARISON BETWEEN THE RESULTS 161
10.4 SUMMARY 162
11 P I T C H - A S Y N C H R O N O U S O V E R L A P - A D D W A V E F O
R M - C O N C A T E N A T I O N S P E E C H S Y N T H E S I S B Y O P T
I M I Z I N G P H A S E S P E C T R U M IN FREQUENCY D O M A I N 163
11.1 PITCH-SYNCHRONOUS AND -ASYNCHRONOUS METHODS 163
11.1.1 PITCH MARK AND PITCH-SYNCHRONOUS METHOD 163
11.1.2 HUMAN SENSES SOUND SPECTRUM 165
11.1.3 PROBLEM IN SIMPLE ASYNCHRONOUS SPEECH SYNTHESIS . . . 165 11.1.4
PITCH-ASYNCHRONOUS METHODS: SINGLE PHASE-ADJUSTMENT METHOD AND STEPWISE
PHASE-ADJUSTMENT METHOD 166
11.1.5 CONVOLUTIONS AND NEURAL NETWORKS 167
11.2 CONSTRUCTION OF STEPWISE PHASE-ADJUSTMENT SYSTEM 167 11.3
OPTIMIZATION OF PULSE SHARPNESS 170
11.4 EXPERIMENTAL RESULTS 172
11.5 SUMMARY 174
CLOSING R E M A R K S 177
REFERENCES 179
INDEX 193 |
| any_adam_object | 1 |
| author | Hirose, Akira 1963- |
| author_GND | (DE-588)132071541 |
| author_facet | Hirose, Akira 1963- |
| author_role | aut |
| author_sort | Hirose, Akira 1963- |
| author_variant | a h ah |
| building | Verbundindex |
| bvnumber | BV040241534 |
| classification_rvk | ST 300 ST 301 |
| ctrlnum | (OCoLC)794530798 (DE-599)DNB1018095152 |
| 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 | Informatik Elektrotechnik / Elektronik / Nachrichtentechnik |
| edition | 2. ed. |
| format | Book |
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| id | DE-604.BV040241534 |
| illustrated | Illustrated |
| indexdate | 2024-07-21T00:34:54Z |
| institution | BVB |
| isbn | 9783642276316 3642276318 9783642435799 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-025097729 |
| oclc_num | 794530798 |
| open_access_boolean | |
| owner | DE-11 DE-473 DE-BY-UBG DE-20 |
| owner_facet | DE-11 DE-473 DE-BY-UBG DE-20 |
| physical | XVII, 197 S. Ill., graph. Darst. |
| publishDate | 2012 |
| publishDateSearch | 2012 |
| publishDateSort | 2012 |
| publisher | Springer |
| record_format | marc |
| series | Studies in computational intelligence |
| series2 | Studies in computational intelligence |
| spelling | Hirose, Akira 1963- Verfasser (DE-588)132071541 aut Complex valued neural networks Akira Hirose Complex-valued neural networks 2. ed. Berlin [u.a.] Springer 2012 XVII, 197 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Studies in computational intelligence 400 Literaturangaben Gehirn (DE-588)4019752-9 gnd rswk-swf Bildverarbeitung (DE-588)4006684-8 gnd rswk-swf Kognitive Komplexität (DE-588)4125180-5 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Komplexe Zahl (DE-588)4128698-4 gnd rswk-swf Komplexes System (DE-588)4114261-5 gnd rswk-swf Soft Computing (DE-588)4455833-8 gnd rswk-swf Neuronales Netz (DE-588)4226127-2 gnd rswk-swf Nervenzelle (DE-588)4041649-5 gnd rswk-swf Biosignalverarbeitung (DE-588)4006899-7 gnd rswk-swf Selbst organisierendes System (DE-588)4054424-2 gnd rswk-swf Neuronales Netz (DE-588)4226127-2 s Komplexe Zahl (DE-588)4128698-4 s DE-604 Maschinelles Lernen (DE-588)4193754-5 s Selbst organisierendes System (DE-588)4054424-2 s Komplexes System (DE-588)4114261-5 s Gehirn (DE-588)4019752-9 s Kognitive Komplexität (DE-588)4125180-5 s Nervenzelle (DE-588)4041649-5 s Biosignalverarbeitung (DE-588)4006899-7 s 1\p DE-604 Bildverarbeitung (DE-588)4006684-8 s Soft Computing (DE-588)4455833-8 s 2\p DE-604 Erscheint auch als Online-Ausgabe Complex-Valued Neural Networks Studies in computational intelligence 400 (DE-604)BV020822171 400 X:MVB text/html http://deposit.dnb.de/cgi-bin/dokserv?id=3941306&prov=M&dok_var=1&dok_ext=htm Inhaltstext DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025097729&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 2\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Hirose, Akira 1963- Complex valued neural networks Studies in computational intelligence Gehirn (DE-588)4019752-9 gnd Bildverarbeitung (DE-588)4006684-8 gnd Kognitive Komplexität (DE-588)4125180-5 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Komplexe Zahl (DE-588)4128698-4 gnd Komplexes System (DE-588)4114261-5 gnd Soft Computing (DE-588)4455833-8 gnd Neuronales Netz (DE-588)4226127-2 gnd Nervenzelle (DE-588)4041649-5 gnd Biosignalverarbeitung (DE-588)4006899-7 gnd Selbst organisierendes System (DE-588)4054424-2 gnd |
| subject_GND | (DE-588)4019752-9 (DE-588)4006684-8 (DE-588)4125180-5 (DE-588)4193754-5 (DE-588)4128698-4 (DE-588)4114261-5 (DE-588)4455833-8 (DE-588)4226127-2 (DE-588)4041649-5 (DE-588)4006899-7 (DE-588)4054424-2 |
| title | Complex valued neural networks |
| title_alt | Complex-valued neural networks |
| title_auth | Complex valued neural networks |
| title_exact_search | Complex valued neural networks |
| title_full | Complex valued neural networks Akira Hirose |
| title_fullStr | Complex valued neural networks Akira Hirose |
| title_full_unstemmed | Complex valued neural networks Akira Hirose |
| title_short | Complex valued neural networks |
| title_sort | complex valued neural networks |
| topic | Gehirn (DE-588)4019752-9 gnd Bildverarbeitung (DE-588)4006684-8 gnd Kognitive Komplexität (DE-588)4125180-5 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Komplexe Zahl (DE-588)4128698-4 gnd Komplexes System (DE-588)4114261-5 gnd Soft Computing (DE-588)4455833-8 gnd Neuronales Netz (DE-588)4226127-2 gnd Nervenzelle (DE-588)4041649-5 gnd Biosignalverarbeitung (DE-588)4006899-7 gnd Selbst organisierendes System (DE-588)4054424-2 gnd |
| topic_facet | Gehirn Bildverarbeitung Kognitive Komplexität Maschinelles Lernen Komplexe Zahl Komplexes System Soft Computing Neuronales Netz Nervenzelle Biosignalverarbeitung Selbst organisierendes System |
| url | http://deposit.dnb.de/cgi-bin/dokserv?id=3941306&prov=M&dok_var=1&dok_ext=htm http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025097729&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV020822171 |
| work_keys_str_mv | AT hiroseakira complexvaluedneuralnetworks |