Издательство Academic Press, 1998, -459 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/ )Orthogonal Functions for Systems Identification and Control
Multilayer Recurrent Neural Networks for Synthesizing and Tuning Linear Control Systems via Pole Assignment
Direct and Indirect Techniques to Control Unknown Nonlinear Dynamical Systems Using Dynamical Neural Networks
A Receding Horizon Optimal Tracking Neurocontroller for Nonlinear Dynamic Systems
On-Line Approximators for Nonlinear System Identification: A Unified Approach
The Determination of Multivariable Nonlinear Models for Dynamic Systems
High-Order Neural Network Systems in the Identification of Dynamical Systems
Neurocontrols for Systems with Unknown Dynamics
On-Line Learning Neural Networks for Aircraft Autopilot and Command Augmentation Systems
Nonlinear System Modeling
