Optimization Techniques, Volume 2

Albertini and Pra, Recurrent Neural Networks: Identification and Other System Theoretic Properties. Anderson and Titterington, Boltzmann Machines: Statistical Associations and Algorithms for Training Anderson and Titterington. Campbell, Constructive Learning Techniques for Designing Neural Network Systems. Mehrotra and Mohan, Modular Neural Networks. Xu and Kwong, Associative Memories. Fry and Sova, A Logical Basis for Neural Network Design.Hoekstra, Duin, and Kraaijveld, Neural Networks Applied to Data Analysis. Zhang and Wang, Multi-Mode Single Neuron Arithmetics.

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.

• 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

Practitioners, research workers, academicians, and students in mechanical, electrical, industrial, manufacturing, and production engineering, as well as computer science and engineering.