Neural networks: computational models and applications ; with 27 tables
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| Main Authors: | , , |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Berlin [u.a.]
Springer
2007
|
| Series: | Studies in computational intelligence
53 |
| Subjects: | |
| Online Access: | Inhaltsverzeichnis Beschreibung für Leser Auszug Auszug Inhaltsverzeichnis Klappentext |
| Physical Description: | XXII, 299 S. Ill., graph. Darst. |
| ISBN: | 9783540692256 3540692258 |
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| 245 | 1 | 0 | |a Neural networks |b computational models and applications ; with 27 tables |c Huajin Tang ; Kay Chen Tan ; Yi Zhang |
| 264 | 1 | |a Berlin [u.a.] |b Springer |c 2007 | |
| 300 | |a XXII, 299 S. |b Ill., graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
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| 490 | 1 | |a Studies in computational intelligence |v 53 | |
| 650 | 4 | |a Neural networks (Computer science) | |
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| 700 | 1 | |a Tan, Kay Chen |d 1960- |e Verfasser |0 (DE-588)133081532 |4 aut | |
| 700 | 1 | |a Yi, Zhang |e Verfasser |0 (DE-588)133081575 |4 aut | |
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Record in the Search Index
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| adam_text | Contents
List of Figures
.................................................
XV
List of Tables
.................................................XXI
1
Introduction
............................................... 1
1.1
Backgrounds
............................................ 1
1.1.1
Feed-forward Neural Networks
...................... 1
1.1.2
Recurrent Networks with Saturating Transfer
Functions
........................................ 2
1.1.3
Recurrent Networks with Nonsatur
ating
Transfer
Functions
........................................ 4
1.2
Scopes
................................................. 5
1.3
Organization
............................................ 6
2
Feedforward Neural Networks and Training Methods
...... 9
2.1
Introduction
............................................ 9
2.2
Error Back-propagation Algorithm
......................... 9
2.3
Optimal Weight Initialization Method
...................... 12
2.4
The Optimization-Layer-by-Layer Algorithm
................ 14
2.4.1
Optimization of the Output Layer
................... 15
2.4.2
Optimization of the Hidden Layer
................... 16
2.5
Modified Error Back-Propagation Method
.................. 18
3
New Dynamical Optimal Learning for Linear Multilayer
FNN
...................................................... 23
3.1
Introduction
............................................ 23
3.2
Preliminaries
............................................ 24
3.3
The Dynamical Optimal Learning
......................... 25
3.4
Simulation Results
....................................... 28
3.4.1
Function Mapping
................................. 28
3.4.2
Pattern Recognition
............................... 30
X
Contents
3.5
Discussions
............................................. 33
3.6
Conclusion
............................................. 33
4 Fundamentals
of Dynamic Systems
........................ 35
4.1
Linear Systems and State Space
........................... 35
4.1.1
Linear Systems in R2
.............................. 35
4.1.2
Linear Systems in Rn
.............................. 38
4.2
Nonlinear Systems
....................................... 41
4.3
Stability, Convergence and Bounded-ness
................... 41
4.4
Analysis of
Neuro-dynamics
............................... 46
4.5
Limit Sets, Attractors and Limit Cycles
.................... 51
5
Various Computational Models and Applications
.......... 57
5.1
RNNs as a Linear and Nonlinear Programming Solver
........ 57
5.1.1
Recurrent Neural Networks
......................... 58
5.1.2
Comparison with Genetic Algorithms
................ 59
5.2
RNN Models for Extracting Eigenvectors
................... 66
5.3
A Discrete-Time Winner-Takes-All Network
................ 68
5.4
A Winner-Takes-All Network with LT Neurons
.............. 70
5.5
Competitive-Layer Model for Feature Binding
and Segmentation
....................................... 74
5.6
A Neural Model of Contour Integration
.................... 76
5.7
Sceno
Segmentation Based on Temporal Correlation
......... 77
6
Convergence Analysis of Discrete Time RNNs for Linear
Variational Inequality Problem
............................ 81
6.1
Introduction
............................................ 81
6.2
Preliminaries
............................................ 82
6.3
Convergence Analysis: A is a Positive
Semidefinite
Matrix
.... 83
6.4
Convergence Analysis: A is a Positive Definite Matrix
........ 85
6.5
Discussions and Simulations
.............................. 87
6.6
Conclusions
............................................. 96
7
Parameter Settings of Hopfield Networks Applied
to Traveling Salesman Problems
........................... 99
7.1
Introduction
............................................ 99
7.2
TSP Mapping and CHN Model
............................100
7.3
The Enhanced Lyapunov Function for Mapping TSP
.........102
7.4
Stability Based Analysis for Network s Activities
............104
7.5
Suppression of Spurious States
............................105
7.6
Setting of Parameters
....................................112
7.7
Simulation Results and Discussions
........................112
7.8
Conclusion
.............................................115
Contents
XI
8
Competitive
Model
for Combinatorial Optimization
Problems
..................................................117
8.1
Introduction
............................................117
8.2
Columnar Competitive Model
.............................118
8.3
Convergence of Competitive Model and Full Valid Solutions
. . 120
8.4
Simulated Annealing Applied to Competitive Model
.........123
8.5
Simulation Results
.......................................124
8.6
Conclusion
.............................................128
9
Competitive Neural Networks for Image Segmentation
.... 129
9.1
Introduction
............................................129
9.2
Neural Networks Based Image Segmentation
................130
9.3
Competitive Model of Neural Networks
.....................131
9.4
Dynamical Stability Analysis
.............................132
9.5
Simulated Annealing Applied to Competitive Model
.........134
9.6
Local Minima Escape Algorithm Applied to Competitive
Model
..................................................135
9.7
Simulation Results
.......................................137
9.7.1
Error-Correcting
..................................137
9.7.2
Image Segmentation
...............................140
9.8
Conclusion
.............................................143
10
Columnar Competitive Model for Solving Multi-Traveling
Salesman Problem
.........................................145
10.1
Introduction
............................................145
10.2
The MTSP Problem
.....................................146
10.3
MTSP Mapping and CCM Model
.........................148
10.4
Valid Solutions and Convergence Analysis of CCM for MTSP
. 151
10.4.1
Parameters Settings for the CCM Applied to MTSP
... 152
10.4.2
Dynamical Stability Analysis
.......................153
10.5
Simulation Results
.......................................156
10.6
Conclusions
.............................................159
11
Improving Local Minima of Columnar Competitive Model
for TSPs
...................................................161
11.1
Introduction
............................................161
11.2
Performance Analysis for CCM
............................162
11.3
An Improving for Columnar Competitive Model
.............165
11.3.1
Some Preliminary Knowledge
.......................165
11.3.2
A Modified Neural Representation for CCM
..........167
11.3.3
The Improvement for Columnar Competitive Model
. . . 168
11.4
Simulation Results
.......................................171
11.5
Conclusions
.............................................174
XII Contents
12
A New Algorithm for Finding the Shortest Paths Using
PCNN
.....................................................177
12.1
Introduction
............................................177
12.2
PCNNs Neuron Model
...................................178
12.3
The Multi-Output Model of Pulse Coupled Neural Networks
(MPCNNs)
.............................................180
12.3.1
The Design of MPCNNs
............................180
12.3.2
Performance Analysis of the Travelling of
Autowaves
in MPCNNs
......................................181
12.4
The Algorithm for Solving the Shortest Path Problems using
MPCNNs
...............................................183
12.5
Simulation Results
.......................................184
12.6
Conclusions
.............................................188
13
Qualitative Analysis for Neural Networks with LT Transfer
Functions
..................................................191
13.1
Introduction
............................................191
13.2
Equilibria and Their Properties
...........................192
13.3
Coexistence of Multiple Equilibria
.........................197
13.4
Boundedness and Global Attractivity
......................199
13.5
Simulation Examples
.....................................203
13.6
Conclusion
.............................................206
14
Analysis of Cyclic Dynamics for Networks
of Linear Threshold Neurons
..............................211
14.1
Introduction
............................................211
14.2
Preliminaries
............................................212
14.3
Geometrical Properties of Equilibria
.......................213
14.4
Neural States in Dx and D2
..............................214
14.4.1
Phase Analysis for Center Type Equilibrium in
Di
.... 214
14.4.2
Phase Analysis in D2
..............................215
14.4.3
Neural States Computed in Temporal Domain
........217
14.5
Rotated Vector Fields
....................................217
14.6
Existence and Boundary of Periodic Orbits
.................219
14.7
Winner-take-all Network
.................................226
14.8
Examples and Discussions
................................229
14.8.1
Nondivergence Arising from A Limit Cycle
............229
14.8.2
An Example of WTA Network
......................229
14.8.3
Periodic Orbits of Center Type
......................229
14.9
Conclusion
.............................................231
15
LT Network Dynamics and Analog Associative Memory
... 235
15.1
Introduction
............................................235
15.2
Linear Threshold Neurons
................................236
15.3
LT Network Dynamics (Revisited)
.........................238
Contents XIII
15.4 Analog
Associative
Memory ..............................243
15.4.1
Methodology
......................................243
15.4.2 Design
Method
....................................244
15.4.3
Strategies of Measures and Interpretation
.............247
15.5
Simulation Results
.......................................247
15.5.1
Small-Scale Example
...............................248
15.5.2
Single Stored Images
...............................249
15.5.3
Multiple Stored Images
............................251
15.6
Discussion
..............................................252
15.6.1
Performance Metrics
...............................252
15.6.2
Competition and Stability
..........................252
15.6.3
Sparsity and Nonlinear Dynamics
...................253
15.7
Conclusion
.............................................255
16
Output Convergence Analysis for Delayed RNN
with Time Varying Inputs
.................................259
16.1
Introduction
............................................259
16.2
Preliminaries
............................................261
16.3
Convergence Analysis
....................................264
16.4
Simulation Results
.......................................274
16.5
Conclusion
.............................................276
17
Background Neural Networks with Uniform Firing Rate
and Background Input
.....................................279
17.1
Introduction
............................................279
17.2
Preliminaries
............................................280
17.3
Nondivergence and Global Attractivity
.....................282
17.4
Complete Stability
.......................................283
17.5
Discussion
..............................................285
17.6
Simulation
..............................................286
17.7
Conclusions
.............................................286
References
.....................................................289
Neural Networks:
Computational Models and Applications covers a wealth of impor¬
tant theoretical and practical issues in neural networks, including the learning
algorithms of feed-forward neural networks, various dynamical properties of
recurrent neural networks, winner-take-all networks and their applications in broad
manifolds of computational intelligence: pattern recognition, uniform approxima¬
tion, constrained optimization, NP-hard problems, and image segmentation. By
presenting various computational models, this book is developed to provide readers
with a quick but insightful understanding of the broad and rapidly growing areas
in the neural networks domain.
Besides laying down fundamentals on artificial neural networks, this book also
studies biologically inspired neural networks. Some typical computational models
are discussed, and subsequently applied to objection recognition, scene analysis
and associative memory. The studies of bio-inspired models have important impli¬
cations in computer vision and robotic navigation, as well as new efficient algo¬
rithms for image analysis. Another significant feature of the book is that it begins
with fundamental dynamical problems in presenting the mathematical techniques
extensively used in analyzing
neurodynamics,
thus allowing non-mathematicians
to develop and apply these analytical techniques easily.
Written for a wide readership, engineers, computer scientists and mathematicians
interested in machine learning, data mining and neural networks modeling will
find this book of value. This book will also act as a helpful reference for graduate
students studying neural networks and complex dynamical systems.
|
| adam_txt |
Contents
List of Figures
.
XV
List of Tables
.XXI
1
Introduction
. 1
1.1
Backgrounds
. 1
1.1.1
Feed-forward Neural Networks
. 1
1.1.2
Recurrent Networks with Saturating Transfer
Functions
. 2
1.1.3
Recurrent Networks with Nonsatur
ating
Transfer
Functions
. 4
1.2
Scopes
. 5
1.3
Organization
. 6
2
Feedforward Neural Networks and Training Methods
. 9
2.1
Introduction
. 9
2.2
Error Back-propagation Algorithm
. 9
2.3
Optimal Weight Initialization Method
. 12
2.4
The Optimization-Layer-by-Layer Algorithm
. 14
2.4.1
Optimization of the Output Layer
. 15
2.4.2
Optimization of the Hidden Layer
. 16
2.5
Modified Error Back-Propagation Method
. 18
3
New Dynamical Optimal Learning for Linear Multilayer
FNN
. 23
3.1
Introduction
. 23
3.2
Preliminaries
. 24
3.3
The Dynamical Optimal Learning
. 25
3.4
Simulation Results
. 28
3.4.1
Function Mapping
. 28
3.4.2
Pattern Recognition
. 30
X
Contents
3.5
Discussions
. 33
3.6
Conclusion
. 33
4 Fundamentals
of Dynamic Systems
. 35
4.1
Linear Systems and State Space
. 35
4.1.1
Linear Systems in R2
. 35
4.1.2
Linear Systems in Rn
. 38
4.2
Nonlinear Systems
. 41
4.3
Stability, Convergence and Bounded-ness
. 41
4.4
Analysis of
Neuro-dynamics
. 46
4.5
Limit Sets, Attractors and Limit Cycles
. 51
5
Various Computational Models and Applications
. 57
5.1
RNNs as a Linear and Nonlinear Programming Solver
. 57
5.1.1
Recurrent Neural Networks
. 58
5.1.2
Comparison with Genetic Algorithms
. 59
5.2
RNN Models for Extracting Eigenvectors
. 66
5.3
A Discrete-Time Winner-Takes-All Network
. 68
5.4
A Winner-Takes-All Network with LT Neurons
. 70
5.5
Competitive-Layer Model for Feature Binding
and Segmentation
. 74
5.6
A Neural Model of Contour Integration
. 76
5.7
Sceno
Segmentation Based on Temporal Correlation
. 77
6
Convergence Analysis of Discrete Time RNNs for Linear
Variational Inequality Problem
. 81
6.1
Introduction
. 81
6.2
Preliminaries
. 82
6.3
Convergence Analysis: A is a Positive
Semidefinite
Matrix
. 83
6.4
Convergence Analysis: A is a Positive Definite Matrix
. 85
6.5
Discussions and Simulations
. 87
6.6
Conclusions
. 96
7
Parameter Settings of Hopfield Networks Applied
to Traveling Salesman Problems
. 99
7.1
Introduction
. 99
7.2
TSP Mapping and CHN Model
.100
7.3
The Enhanced Lyapunov Function for Mapping TSP
.102
7.4
Stability Based Analysis for Network's Activities
.104
7.5
Suppression of Spurious States
.105
7.6
Setting of Parameters
.112
7.7
Simulation Results and Discussions
.112
7.8
Conclusion
.115
Contents
XI
8
Competitive
Model
for Combinatorial Optimization
Problems
.117
8.1
Introduction
.117
8.2
Columnar Competitive Model
.118
8.3
Convergence of Competitive Model and Full Valid Solutions
. . 120
8.4
Simulated Annealing Applied to Competitive Model
.123
8.5
Simulation Results
.124
8.6
Conclusion
.128
9
Competitive Neural Networks for Image Segmentation
. 129
9.1
Introduction
.129
9.2
Neural Networks Based Image Segmentation
.130
9.3
Competitive Model of Neural Networks
.131
9.4
Dynamical Stability Analysis
.132
9.5
Simulated Annealing Applied to Competitive Model
.134
9.6
Local Minima Escape Algorithm Applied to Competitive
Model
.135
9.7
Simulation Results
.137
9.7.1
Error-Correcting
.137
9.7.2
Image Segmentation
.140
9.8
Conclusion
.143
10
Columnar Competitive Model for Solving Multi-Traveling
Salesman Problem
.145
10.1
Introduction
.145
10.2
The MTSP Problem
.146
10.3
MTSP Mapping and CCM Model
.148
10.4
Valid Solutions and Convergence Analysis of CCM for MTSP
. 151
10.4.1
Parameters Settings for the CCM Applied to MTSP
. 152
10.4.2
Dynamical Stability Analysis
.153
10.5
Simulation Results
.156
10.6
Conclusions
.159
11
Improving Local Minima of Columnar Competitive Model
for TSPs
.161
11.1
Introduction
.161
11.2
Performance Analysis for CCM
.162
11.3
An Improving for Columnar Competitive Model
.165
11.3.1
Some Preliminary Knowledge
.165
11.3.2
A Modified Neural Representation for CCM
.167
11.3.3
The Improvement for Columnar Competitive Model
. . . 168
11.4
Simulation Results
.171
11.5
Conclusions
.174
XII Contents
12
A New Algorithm for Finding the Shortest Paths Using
PCNN
.177
12.1
Introduction
.177
12.2
PCNNs Neuron Model
.178
12.3
The Multi-Output Model of Pulse Coupled Neural Networks
(MPCNNs)
.180
12.3.1
The Design of MPCNNs
.180
12.3.2
Performance Analysis of the Travelling of
Autowaves
in MPCNNs
.181
12.4
The Algorithm for Solving the Shortest Path Problems using
MPCNNs
.183
12.5
Simulation Results
.184
12.6
Conclusions
.188
13
Qualitative Analysis for Neural Networks with LT Transfer
Functions
.191
13.1
Introduction
.191
13.2
Equilibria and Their Properties
.192
13.3
Coexistence of Multiple Equilibria
.197
13.4
Boundedness and Global Attractivity
.199
13.5
Simulation Examples
.203
13.6
Conclusion
.206
14
Analysis of Cyclic Dynamics for Networks
of Linear Threshold Neurons
.211
14.1
Introduction
.211
14.2
Preliminaries
.212
14.3
Geometrical Properties of Equilibria
.213
14.4
Neural States in Dx and D2
.214
14.4.1
Phase Analysis for Center Type Equilibrium in
Di
. 214
14.4.2
Phase Analysis in D2
.215
14.4.3
Neural States Computed in Temporal Domain
.217
14.5
Rotated Vector Fields
.217
14.6
Existence and Boundary of Periodic Orbits
.219
14.7
Winner-take-all Network
.226
14.8
Examples and Discussions
.229
14.8.1
Nondivergence Arising from A Limit Cycle
.229
14.8.2
An Example of WTA Network
.229
14.8.3
Periodic Orbits of Center Type
.229
14.9
Conclusion
.231
15
LT Network Dynamics and Analog Associative Memory
. 235
15.1
Introduction
.235
15.2
Linear Threshold Neurons
.236
15.3
LT Network Dynamics (Revisited)
.238
Contents XIII
15.4 Analog
Associative
Memory .243
15.4.1
Methodology
.243
15.4.2 Design
Method
.244
15.4.3
Strategies of Measures and Interpretation
.247
15.5
Simulation Results
.247
15.5.1
Small-Scale Example
.248
15.5.2
Single Stored Images
.249
15.5.3
Multiple Stored Images
.251
15.6
Discussion
.252
15.6.1
Performance Metrics
.252
15.6.2
Competition and Stability
.252
15.6.3
Sparsity and Nonlinear Dynamics
.253
15.7
Conclusion
.255
16
Output Convergence Analysis for Delayed RNN
with Time Varying Inputs
.259
16.1
Introduction
.259
16.2
Preliminaries
.261
16.3
Convergence Analysis
.264
16.4
Simulation Results
.274
16.5
Conclusion
.276
17
Background Neural Networks with Uniform Firing Rate
and Background Input
.279
17.1
Introduction
.279
17.2
Preliminaries
.280
17.3
Nondivergence and Global Attractivity
.282
17.4
Complete Stability
.283
17.5
Discussion
.285
17.6
Simulation
.286
17.7
Conclusions
.286
References
.289
Neural Networks:
Computational Models and Applications covers a wealth of impor¬
tant theoretical and practical issues in neural networks, including the learning
algorithms of feed-forward neural networks, various dynamical properties of
recurrent neural networks, winner-take-all networks and their applications in broad
manifolds of computational intelligence: pattern recognition, uniform approxima¬
tion, constrained optimization, NP-hard problems, and image segmentation. By
presenting various computational models, this book is developed to provide readers
with a quick but insightful understanding of the broad and rapidly growing areas
in the neural networks domain.
Besides laying down fundamentals on artificial neural networks, this book also
studies biologically inspired neural networks. Some typical computational models
are discussed, and subsequently applied to objection recognition, scene analysis
and associative memory. The studies of bio-inspired models have important impli¬
cations in computer vision and robotic navigation, as well as new efficient algo¬
rithms for image analysis. Another significant feature of the book is that it begins
with fundamental dynamical problems in presenting the mathematical techniques
extensively used in analyzing
neurodynamics,
thus allowing non-mathematicians
to develop and apply these analytical techniques easily.
Written for a wide readership, engineers, computer scientists and mathematicians
interested in machine learning, data mining and neural networks modeling will
find this book of value. This book will also act as a helpful reference for graduate
students studying neural networks and complex dynamical systems. |
| any_adam_object | 1 |
| any_adam_object_boolean | 1 |
| author | Tang, Huajin Tan, Kay Chen 1960- Yi, Zhang |
| author_GND | (DE-588)133081583 (DE-588)133081532 (DE-588)133081575 |
| author_facet | Tang, Huajin Tan, Kay Chen 1960- Yi, Zhang |
| author_role | aut aut aut |
| author_sort | Tang, Huajin |
| author_variant | h t ht k c t kc kct z y zy |
| building | Verbundindex |
| bvnumber | BV022380229 |
| callnumber-first | Q - Science |
| callnumber-label | QA76 |
| callnumber-raw | QA76.87 |
| callnumber-search | QA76.87 |
| callnumber-sort | QA 276.87 |
| callnumber-subject | QA - Mathematics |
| classification_rvk | ST 301 |
| ctrlnum | (OCoLC)180711153 (DE-599)BVBBV022380229 |
| 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 |
| discipline_str_mv | Maschinenbau / Maschinenwesen Informatik |
| format | Book |
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| id | DE-604.BV022380229 |
| illustrated | Illustrated |
| index_date | 2024-07-02T17:11:05Z |
| indexdate | 2024-07-09T20:56:22Z |
| institution | BVB |
| isbn | 9783540692256 3540692258 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-015589227 |
| oclc_num | 180711153 |
| open_access_boolean | |
| owner | DE-706 DE-703 DE-1051 DE-355 DE-BY-UBR DE-634 DE-83 |
| owner_facet | DE-706 DE-703 DE-1051 DE-355 DE-BY-UBR DE-634 DE-83 |
| physical | XXII, 299 S. Ill., graph. Darst. |
| publishDate | 2007 |
| publishDateSearch | 2007 |
| publishDateSort | 2007 |
| publisher | Springer |
| record_format | marc |
| series | Studies in computational intelligence |
| series2 | Studies in computational intelligence |
| spelling | Tang, Huajin Verfasser (DE-588)133081583 aut Neural networks computational models and applications ; with 27 tables Huajin Tang ; Kay Chen Tan ; Yi Zhang Berlin [u.a.] Springer 2007 XXII, 299 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Studies in computational intelligence 53 Neural networks (Computer science) Neuronales Netz (DE-588)4226127-2 gnd rswk-swf Neuronales Netz (DE-588)4226127-2 s DE-604 Tan, Kay Chen 1960- Verfasser (DE-588)133081532 aut Yi, Zhang Verfasser (DE-588)133081575 aut Studies in computational intelligence 53 (DE-604)BV020822171 53 http://swbplus.bsz-bw.de/bsz273061135inh.htm Inhaltsverzeichnis http://catdir.loc.gov/catdir/enhancements/fy0824/2006939344-d.html Beschreibung für Leser http://swbplus.bsz-bw.de/bsz273061135vor.htm Auszug http://swbplus.bsz-bw.de/bsz273061135kap.htm Auszug Digitalisierung UB Regensburg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015589227&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Digitalisierung UB Regensburg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015589227&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Klappentext |
| spellingShingle | Tang, Huajin Tan, Kay Chen 1960- Yi, Zhang Neural networks computational models and applications ; with 27 tables Studies in computational intelligence Neural networks (Computer science) Neuronales Netz (DE-588)4226127-2 gnd |
| subject_GND | (DE-588)4226127-2 |
| title | Neural networks computational models and applications ; with 27 tables |
| title_auth | Neural networks computational models and applications ; with 27 tables |
| title_exact_search | Neural networks computational models and applications ; with 27 tables |
| title_exact_search_txtP | Neural networks computational models and applications ; with 27 tables |
| title_full | Neural networks computational models and applications ; with 27 tables Huajin Tang ; Kay Chen Tan ; Yi Zhang |
| title_fullStr | Neural networks computational models and applications ; with 27 tables Huajin Tang ; Kay Chen Tan ; Yi Zhang |
| title_full_unstemmed | Neural networks computational models and applications ; with 27 tables Huajin Tang ; Kay Chen Tan ; Yi Zhang |
| title_short | Neural networks |
| title_sort | neural networks computational models and applications with 27 tables |
| title_sub | computational models and applications ; with 27 tables |
| topic | Neural networks (Computer science) Neuronales Netz (DE-588)4226127-2 gnd |
| topic_facet | Neural networks (Computer science) Neuronales Netz |
| url | http://swbplus.bsz-bw.de/bsz273061135inh.htm http://catdir.loc.gov/catdir/enhancements/fy0824/2006939344-d.html http://swbplus.bsz-bw.de/bsz273061135vor.htm http://swbplus.bsz-bw.de/bsz273061135kap.htm http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015589227&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015589227&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV020822171 |
| work_keys_str_mv | AT tanghuajin neuralnetworkscomputationalmodelsandapplicationswith27tables AT tankaychen neuralnetworkscomputationalmodelsandapplicationswith27tables AT yizhang neuralnetworkscomputationalmodelsandapplicationswith27tables |
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