Издательство Academic Press, 1998, -423 pp.Inspired by the structure of the human brain, artificial neural networks have been widely applied to fields such as pattern recognition, optimization, coding, control, etc., because of their ability to solve cumbersome or intractable problems by learning directly from data. An artificial neural network usually consists of a large number of simple processing units, i.e., neurons, via mutual interconnection. It learns to solve problems by adequately adjusting the strength of the interconnections according to input data. Moreover, the neural network adapts easily to new environments by learning, and can deal with information that is noisy, inconsistent, vague, or probabilistic. These features have motivated extensive research and developments in artificial neural networks. This volume is probably the first rather comprehensive treatment devoted to the broad areas of algorithms and architectures for the realization of neural network systems. Techniques and diverse methods in numerous areas of this broad subject are presented. In addition, various major neural network structures for achieving effective systems are presented and illustrated by examples in all cases. Numerous other techniques and subjects related to this broadly significant area are treated.
The remarkable breadth and depth of the advances in neural network systems with their many substantive applications both realized and yet to be realized, make it quite evident that adequate treatment of this broad area requires a number of distinctly titled but well-integrated volumes. This is the fifth of seven volumes on the subject of neural network systems and it is entitled Image Processing and Pattern Recognition. The entire set of seven volumes contains
Algorithms and Architectures ( /file/1517411/ or /file/261251/ )
Optimization Techniques ( /file/664172/ )
Implementation Techniques ( /file/664174/ )
Industrial and Manufacturing Systems (absent)
Image Processing and Pattern Recognition ( /file/664149/ )
Fuzzy Logic and Expert Systems Applications ( /file/664164/ )
Control and Dynamic Systems ( /file/664176/ )Optimal Learning in Artificial Neural Networks: A Theoretical View
Orthogonal Transformation Techniques in the Optimization of Feedforward Neural Network Systems
Sequential Constructive Techniques
Fast Backpropagation Training Using Optimal Learning Rate and Momentum
Learning of Nonstationary Processes
Constraint Satisfaction Problems
Dominant Neuron Techniques
CMAC-Based Techniques for Adaptive Learning Control
Information Dynamics and Neural Techniques for Data Analysis
Radial Basis Function Network Approximation and Learning in Task-Dependent Feedforward Control of Nonlinear Dynamical Systems
