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 "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. It provides in-depth treatment of theoretical contributions to optimal learning for neural network systems and offers a comprehensive treatment of orthogonal transformation techniques for the optimization of neural network systems.; It includes illustrative examples and comprehensive treatment of sequential constructive techniques for optimization of neural network systems and presents a uniquely comprehensive treatment of the highly effective fast back propagation algorithms for the optimization of neural network systems. It treats, in detail, optimization techniques for neural network systems with nonstationary or dynamic inputs. It 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. In the last few years impressive efforts have been made in using connectionist models either for modeling human behavior or for solving practical problems.
