Neural Networks: Computational Models and Applications presents important 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 approximation, constrained optimization, NP-hard problems, and image segmentation. The book offers a compact, insightful understanding of the broad and rapidly growing neural networks domain.
Author(s): Huajin Tang, Kay Chen Tan, Zhang Yi
Series: Studies in Computational Intelligence 53
Edition: 1
Publisher: Springer
Year: 2007
Language: English
Pages: 322
Cover......Page 1
Studies in Computational Intelligence, Volume 53......Page 3
Neural Networks:
Computational Models
and Applications......Page 4
ISBN-13 9783540692256......Page 5
Preface......Page 7
Contents......Page 9
List of Figures......Page 14
List of Tables......Page 19
1
Introduction......Page 21
2
Feedforward Neural Networks and Training
Methods......Page 28
3
New Dynamical Optimal Learning for Linear
Multilayer FNN......Page 41
4
Fundamentals of Dynamic Systems......Page 53
5
Various Computational Models
and Applications......Page 75
6
Convergence Analysis of Discrete Time RNNs
for Linear Variational Inequality Probl......Page 98
7
Parameter Settings of Hopfield Networks
Applied to Traveling Salesman Problems......Page 115
8
Competitive Model for Combinatorial
Optimization Problems......Page 133
9
Competitive Neural Networks for Image
Segmentation......Page 145
10
Columnar Competitive Model for Solving
Multi-Traveling Salesman Problem......Page 161
11
Improving Local Minima of Columnar
Competitive Model for TSPs......Page 177
12
A New Algorithm for Finding the Shortest
Paths Using PCNN......Page 192
13
Qualitative Analysis for Neural Networks
with LT Transfer Functions......Page 205
14
Analysis of Cyclic Dynamics for Networks
of Linear Threshold Neurons......Page 224
15
LT Network Dynamics and Analog Associative
Memory......Page 248
16
Output Convergence Analysis for Delayed
RNN with Time Varying Inputs......Page 271
17
Background Neural Networks with Uniform
Firing Rate and Background Input......Page 290
References......Page 300
