Description
Book SynopsisA topical introduction on the ability of artificial neural networks to not only solve on-line a wide range of optimization problems but also to create new techniques and architectures. Provides in-depth coverage of mathematical modeling along with illustrative computer simulation results.
Table of ContentsMathematical Preliminaries of Neurocomputing.
Architectures and Electronic Implementation of Neural Network Models.
Unconstrained Optimization and Learning Algorithms.
Neural Networks for Linear, Quadratic Programming and Linear Complementarity Problems.
A Neural Network Approach to the On-Line Solution of a System of Linear Algebraic Equations and Related Problems.
Neural Networks for Matrix Algebra Problems.
Neural Networks for Continuous, Nonlinear, Constrained Optimization Problems.
Neural Networks for Estimation, Identification and Prediction.
Neural Networks for Discrete and Combinatorial Optimization Problems.
Appendices.
Subject Index.
