Principles of artificial neural networks:
Saved in:
| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Hackensach, NJ [u.a.]
World Scientific
2007
|
| Edition: | 2. ed. |
| Series: | Advanced series on circuits and systems
6 |
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Item Description: | Includes bibliographical references (p. 291-297) and indexes |
| Physical Description: | XV, 303 S. graph. Darst. |
| ISBN: | 9812706240 9789812706249 |
Staff View
MARC
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| 245 | 1 | 0 | |a Principles of artificial neural networks |c Daniel Graupe |
| 250 | |a 2. ed. | ||
| 264 | 1 | |a Hackensach, NJ [u.a.] |b World Scientific |c 2007 | |
| 300 | |a XV, 303 S. |b graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
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| 490 | 1 | |a Advanced series on circuits and systems |v 6 | |
| 500 | |a Includes bibliographical references (p. 291-297) and indexes | ||
| 650 | 4 | |a Redes Neuronales (Informática) | |
| 650 | 4 | |a Neural networks (Computer science) | |
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Record in the Search Index
| _version_ | 1804138357817606144 |
|---|---|
| adam_text | Contents
Acknowledgments
vii
Preface to the First Edition
ix
Preface to the Second Edition
xi
Chapter
1.
Introduction and Role of Artificial Neural Networks
1
Chapter
2.
Fundamentals of Biological Neural Networks
5
Chapter
3.
Basic Principles of ANNs and Their Early Structures
9
3.1.
Basic Principles of ANN Design
............... 9
3.2.
Basic Network Structures
.................. 10
3.3.
The Perceptron s Input-Output Principles
......... 11
3.4.
The
Adaline (ALC) ..................... 12
Chapter
4.
The Perceptron
17
4.1.
The Basic Structure
..................... 17
4.2.
The Single-Layer Representation Problem
......... 22
4.3.
The Limitations of the Single-Layer Perceptron
...... 23
4.4.
Many-Layer Perceptrons
................... 24
4.
A. Perceptron Case Study: Identifying
Autoregressive
Parameters of a Signal
(AR
Time Series Identification)
. . 25
Chapter
5.
The Madaline
37
5.1.
Madaline Training
...................... 37
5.
A. Madaline Case Study: Character Recognition
....... 39
Chapter
6.
Back Propagation
59
6.1.
The Back Propagation Learning Procedure
........ 59
6.2.
Derivation of the BP Algorithm
............... 59
6.3.
Modified BP Algorithms
................... 63
6.
A. Back Propagation Case Study: Character Recognition
. . 65
xiv
Principles of Artificial and Neural Networks
6.B. Back Propagation Case Study: The Exclusive-OR (XOR)
Problem (2-Layer BP)
.................... 76
6.C. Back Propagation Case Study: The XOR Problem
—
3
Layer BP Network
..................... 94
Chapter
7.
Hopfield Networks
113
7.1.
Introduction
.......................... 113
7.2.
Binary Hopfield Networks
.................. 113
7.3.
Setting of Weights in Hopfield Nets
—
Bidirectional
Associative Memory (BAM) Principle
........... 114
7.4.
Walsh Functions
....................... 117
7.5.
Network Stability
....................... 118
7.6.
Summary of the Procedure for Implementing the
Hopfield Network
....................... 121
7.7.
Continuous Hopfield Models
................. 122
7.8.
The Continuous Energy (Lyapunov) Function
....... 123
7.
A. Hopfield Network Case Study: Character Recognition
. . 125
7.B. Hopfield Network Case Study: Traveling Salesman
Problem
............................ 136
Chapter
8.
Counter Propagation
161
8.1.
Introduction
.......................... 161
8.2.
Kohonen Self-Organizing Map
(SOM)
Layer
........ 161
8.3.
Grossberg
Layer
....................... 162
8.4.
Training of the Kohonen Layer
............... 162
8.5.
Training of
Grossberg
Layers
................ 165
8.6.
The Combined Counter Propagation Network
....... 165
8.
A. Counter Propagation Network Case Study. Character
Recognition
.......................... 166
Chapter
9.
Adaptive Resonance Theory
179
9.1.
Motivation
.......................... 179
9.2.
The ART Network Structure
................ 179
9.3.
Setting-Up of the ART Network
.............. 183
9.4.
Network Operation
...................... 184
9.5.
Properties of ART
...................... 186
9.6.
Discussion and General Comments on ART-I and ART-II
186
9.A. ART-I Network Case Study: Character Recognition
... 187
9.B. ART-I Case Study: Speech Recognition
.......... 201
Chapter
10.
The Cognitron and the Neocognitron
209
10.1.
Background of the Cognitron
................ 209
10.2.
The Basic Principles of the Cognitron
........... 209
Contents xv
10.3. Network Operation......................209
10.4. Cognitron s Network Training................211
10.5. The Neocognitron ......................213
Chapter
11.
Statistical
Training 215
11.1.
Fundamental Philosophy
................... 215
11.2.
Annealing Methods
...................... 216
11.3.
Simulated Annealing by Boltzman Training of Weights
. . 216
11.4.
Stochastic Determination of Magnitude of Weight Change
217
11.5.
Temperature-Equivalent Setting
............... 217
11.6.
Cauchy Training of Neural Network
............ 217
H.A.
Statistical Training Case Study
—
A Stochastic Hopfield
Network for Character Recognition
.............219
ll.B. Statistical Training Case Study: Identifying
AR
Signal
Parameters with a Stochastic Perceptron Model
......222
Chapter
12.
Recurrent (Time Cycling) Back Propagation Networks
233
12.1.
Recurrent/Discrete Time Networks
............. 233
12.2.
Fully Recurrent Networks
.................. 234
12.3.
Continuously Recurrent Back Propagation Networks
. . . 235
12.
A. Recurrent Back Propagation Case Study: Character
Recognition
..........................236
Chapter
13.
Large Scale Memory Storage and Retrieval (LAMSTAR)
Network
249
13.1.
Basic Principles of the LAMSTAR Neural Network
.... 249
13.2.
Detailed Outline of the LAMSTAR Network
........ 251
13.3.
Forgetting Feature
...................... 257
13.4.
Training vs. Operational Runs
............... 258
13.5.
Advanced Data Analysis Capabilities
............ 259
13.6.
Correlation, Interpolation, Extrapolation and
Innovation-Detection
..................... 261
13.7.
Concluding Comments and Discussion of Applicability
. . 262
13.A. LAMSTAR Network Case Study: Character Recognition
. 265
13.B. Application to Medical Diagnosis Problems
........280
Problems
285
References
291
Author Index
299
Subject Index
301
|
| adam_txt |
Contents
Acknowledgments
vii
Preface to the First Edition
ix
Preface to the Second Edition
xi
Chapter
1.
Introduction and Role of Artificial Neural Networks
1
Chapter
2.
Fundamentals of Biological Neural Networks
5
Chapter
3.
Basic Principles of ANNs and Their Early Structures
9
3.1.
Basic Principles of ANN Design
. 9
3.2.
Basic Network Structures
. 10
3.3.
The Perceptron's Input-Output Principles
. 11
3.4.
The
Adaline (ALC) . 12
Chapter
4.
The Perceptron
17
4.1.
The Basic Structure
. 17
4.2.
The Single-Layer Representation Problem
. 22
4.3.
The Limitations of the Single-Layer Perceptron
. 23
4.4.
Many-Layer Perceptrons
. 24
4.
A. Perceptron Case Study: Identifying
Autoregressive
Parameters of a Signal
(AR
Time Series Identification)
. . 25
Chapter
5.
The Madaline
37
5.1.
Madaline Training
. 37
5.
A. Madaline Case Study: Character Recognition
. 39
Chapter
6.
Back Propagation
59
6.1.
The Back Propagation Learning Procedure
. 59
6.2.
Derivation of the BP Algorithm
. 59
6.3.
Modified BP Algorithms
. 63
6.
A. Back Propagation Case Study: Character Recognition
. . 65
xiv
Principles of Artificial and Neural Networks
6.B. Back Propagation Case Study: The Exclusive-OR (XOR)
Problem (2-Layer BP)
. 76
6.C. Back Propagation Case Study: The XOR Problem
—
3
Layer BP Network
. 94
Chapter
7.
Hopfield Networks
113
7.1.
Introduction
. 113
7.2.
Binary Hopfield Networks
. 113
7.3.
Setting of Weights in Hopfield Nets
—
Bidirectional
Associative Memory (BAM) Principle
. 114
7.4.
Walsh Functions
. 117
7.5.
Network Stability
. 118
7.6.
Summary of the Procedure for Implementing the
Hopfield Network
. 121
7.7.
Continuous Hopfield Models
. 122
7.8.
The Continuous Energy (Lyapunov) Function
. 123
7.
A. Hopfield Network Case Study: Character Recognition
. . 125
7.B. Hopfield Network Case Study: Traveling Salesman
Problem
. 136
Chapter
8.
Counter Propagation
161
8.1.
Introduction
. 161
8.2.
Kohonen Self-Organizing Map
(SOM)
Layer
. 161
8.3.
Grossberg
Layer
. 162
8.4.
Training of the Kohonen Layer
. 162
8.5.
Training of
Grossberg
Layers
. 165
8.6.
The Combined Counter Propagation Network
. 165
8.
A. Counter Propagation Network Case Study. Character
Recognition
. 166
Chapter
9.
Adaptive Resonance Theory
179
9.1.
Motivation
. 179
9.2.
The ART Network Structure
. 179
9.3.
Setting-Up of the ART Network
. 183
9.4.
Network Operation
. 184
9.5.
Properties of ART
. 186
9.6.
Discussion and General Comments on ART-I and ART-II
186
9.A. ART-I Network Case Study: Character Recognition
. 187
9.B. ART-I Case Study: Speech Recognition
. 201
Chapter
10.
The Cognitron and the Neocognitron
209
10.1.
Background of the Cognitron
. 209
10.2.
The Basic Principles of the Cognitron
. 209
Contents xv
10.3. Network Operation.209
10.4. Cognitron's Network Training.211
10.5. The Neocognitron .213
Chapter
11.
Statistical
Training 215
11.1.
Fundamental Philosophy
. 215
11.2.
Annealing Methods
. 216
11.3.
Simulated Annealing by Boltzman Training of Weights
. . 216
11.4.
Stochastic Determination of Magnitude of Weight Change
217
11.5.
Temperature-Equivalent Setting
. 217
11.6.
Cauchy Training of Neural Network
. 217
H.A.
Statistical Training Case Study
—
A Stochastic Hopfield
Network for Character Recognition
.219
ll.B. Statistical Training Case Study: Identifying
AR
Signal
Parameters with a Stochastic Perceptron Model
.222
Chapter
12.
Recurrent (Time Cycling) Back Propagation Networks
233
12.1.
Recurrent/Discrete Time Networks
. 233
12.2.
Fully Recurrent Networks
. 234
12.3.
Continuously Recurrent Back Propagation Networks
. . . 235
12.
A. Recurrent Back Propagation Case Study: Character
Recognition
.236
Chapter
13.
Large Scale Memory Storage and Retrieval (LAMSTAR)
Network
249
13.1.
Basic Principles of the LAMSTAR Neural Network
. 249
13.2.
Detailed Outline of the LAMSTAR Network
. 251
13.3.
Forgetting Feature
. 257
13.4.
Training vs. Operational Runs
. 258
13.5.
Advanced Data Analysis Capabilities
. 259
13.6.
Correlation, Interpolation, Extrapolation and
Innovation-Detection
. 261
13.7.
Concluding Comments and Discussion of Applicability
. . 262
13.A. LAMSTAR Network Case Study: Character Recognition
. 265
13.B. Application to Medical Diagnosis Problems
.280
Problems
285
References
291
Author Index
299
Subject Index
301 |
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| index_date | 2024-07-02T22:58:50Z |
| indexdate | 2024-07-09T21:26:55Z |
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| isbn | 9812706240 9789812706249 |
| language | English |
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| physical | XV, 303 S. graph. Darst. |
| publishDate | 2007 |
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| series | Advanced series on circuits and systems |
| series2 | Advanced series on circuits and systems |
| spelling | Graupe, Daniel Verfasser aut Principles of artificial neural networks Daniel Graupe 2. ed. Hackensach, NJ [u.a.] World Scientific 2007 XV, 303 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Advanced series on circuits and systems 6 Includes bibliographical references (p. 291-297) and indexes Redes Neuronales (Informática) Neural networks (Computer science) Neuronales Netz (DE-588)4226127-2 gnd rswk-swf (DE-588)4151278-9 Einführung gnd-content Neuronales Netz (DE-588)4226127-2 s DE-604 Advanced series on circuits and systems 6 (DE-604)BV016934728 6 Digitalisierung UB Bayreuth application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016990235&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Graupe, Daniel Principles of artificial neural networks Advanced series on circuits and systems Redes Neuronales (Informática) Neural networks (Computer science) Neuronales Netz (DE-588)4226127-2 gnd |
| subject_GND | (DE-588)4226127-2 (DE-588)4151278-9 |
| title | Principles of artificial neural networks |
| title_auth | Principles of artificial neural networks |
| title_exact_search | Principles of artificial neural networks |
| title_exact_search_txtP | Principles of artificial neural networks |
| title_full | Principles of artificial neural networks Daniel Graupe |
| title_fullStr | Principles of artificial neural networks Daniel Graupe |
| title_full_unstemmed | Principles of artificial neural networks Daniel Graupe |
| title_short | Principles of artificial neural networks |
| title_sort | principles of artificial neural networks |
| topic | Redes Neuronales (Informática) Neural networks (Computer science) Neuronales Netz (DE-588)4226127-2 gnd |
| topic_facet | Redes Neuronales (Informática) Neural networks (Computer science) Neuronales Netz Einführung |
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