Neural Network Systems Techniques and Applications. Volume 2. Optimization Techniques

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/) /file/1517411/ or (/file/261251/) /file/261251/ ) Optimization Techniques ( (/file/664172/) /file/664172/ ) Implementation Techniques ( (/file/664174/) /file/664174/ ) Industrial and Manufacturing Systems (absent) Image Processing and Pattern Recognition ( (/file/664149/) /file/664149/ ) Fuzzy Logic and Expert Systems Applications ( (/file/664164/) /file/664164/ ) Control and Dynamic Systems ( (/file/664176/) /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 Optimization Techniques is a unique reference source to a diverse array of methods for achieving optimization, and includes both systems structures and computational methods. The text devotes broad coverage toa unified view of optimal learning, orthogonal transformation techniques, sequential constructive techniques, fast back propagation algorithms, techniques for neural networks with nonstationary or dynamic outputs, applications to constraint satisfaction,optimization issues and techniques for unsupervised learning neural networks, optimum Cerebellar Model of Articulation Controller systems, a new statistical theory of optimum neural learning, and the role of the Radial Basis Function in nonlinear dynamical systems.This volume is useful for practitioners, researchers, and students in industrial, manufacturing, mechanical, electrical, and computer engineering.

Key Features
* Provides in-depth treatment of theoretical contributions to optimal learning for neural network systems
* Offers a comprehensive treatment of orthogonal transformation techniques for the optimization of neural network systems
* Includes illustrative examples and comprehensive treatment of sequential constructive techniques for optimization of neural network systems
* Presents a uniquely comprehensive treatment of the highly effective fast back propagation algorithms for the optimization of neural network systems
* Treats, in detail, optimization techniques for neural network systems with nonstationary or dynamic inputs
* Covers optimization techniques and applications of neural network systems in constraint satisfaction This volume covers the integration of fuzzy logic and expert systems. A vital resource in the field, it includes techniques for applying fuzzy systems to neural networks for modeling and control, systematic design procedures for realizing fuzzy neural systems, techniques for the design of rule-based expert systems using the massively parallel processing capabilities of neural networks, the transformation of neural systems into rule-based expert systems, the characteristics and relative merits of integrating fuzzy sets, neural networks, genetic algorithms, and rough sets, and applications to system identification and control as well as nonparametric, nonlinear estimation.Practitioners, researchers, and students in industrial, manufacturing, electrical, and mechanical engineering, as well as computer scientists and engineers will appreciate this reference source to diverse application methodologies. Optimization Techniques is a unique reference source to a diverse array of methods for achieving optimization, and includes both systems structures and computational methods. The text devotes broad coverage to a unified view of optimal learning, orthogonal transformation techniques, sequential constructive techniques, fast back propagation algorithms, techniques for neural networks with nonstationary or dynamic outputs, applications to constraint satisfaction, optimization issues and techniques for unsupervised learning neural networks, optimum Cerebellar Model of Articulation Controller systems, a new statistical theory of optimum neural learning, and the role of the Radial Basis Function in nonlinear dynamical systems. This volume is useful for practitioners, researchers, and students in industrial, manufacturing, mechanical, electrical, and computer engineering. In the last few years impressive efforts have been made in using connectionist models either for modeling human behavior or for solving practical problems.