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Library | Item Barcode | Call Number | Material Type | Item Category 1 | Status |
|---|---|---|---|---|---|
Searching... | 30000010157216 | QA76.87 C524 1993 | Open Access Book | Book | Searching... |
Searching... | 30000010157223 | QA76.87 C524 1993 | Open Access Book | Book | Searching... |
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Summary
Summary
A 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.
Author Notes
Andrzej Cichocki received the M.Sc. (with honors), Ph.D. and Dr.Sc. (Habilitation) degrees, all in electrical engineering, from Warsaw University of Technology in Poland.
Since 1972, he has been with the Institute of Theory of Electrical Engineering, Measurement and Information Systems, Faculty of Electrical Engineering at the Warsaw University of Technology, where he obtain a title of a full Professor in 1995.
He spent several years at University Erlangen-Nuerenberg in Germany, at the Chair of Applied and Theoretical Electrical Engineering directed by Professor Rolf Unbehauen, as an Alexander-von-Humboldt Research Fellow and Guest Professor. In 1995-1997 he was a team leader of the laboratory for Artificial Brain Systems, at Frontier Research Program RIKEN (Japan), in the Brain Information Processing Group.
Reviews 1
Choice Review
A timely and superbly written book on an exciting and emerging field blending the novelty of artificial neural network technology with areas of constrained and unconstrained optimization and digital signal processing. Although the application of neural networks to optimization is still in its infancy, the authors have produced a seminal work that is certain to be a classic for years to come. The book begins with a solid and extended review of the mathematical foundation required for in-depth algorithm analysis. Following a brief introduction to the various types of artificial neural networks, Cichocki and Unbehauen present a detailed treatment on learning algorithms for unconstrained and constrained optimization and linear programming. The latter part of the book deals with use of neural networks on matrix algebra problems, estimation and identification, and combinatorial optimization. This technical work is comprehensive and provides a solid mathematical foundation for future research in this field. A must in technical and professional libraries. Graduate through professional. J. Y. Cheung; University of Oklahoma
Table of Contents
| Mathematical 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 |
