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Optimization Techniques (Volume 2) (Neural Network Systems Techniques and Applications, Volume 2) - Softcover
Synopsis
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
"synopsis" may belong to another edition of this title.
About the Author
Cornelius T. Leondes received his B.S., M.S., and Ph.D. from the University of Pennsylvania and has held numerous positions in industrial and academic institutions. He is currently a Professor Emeritus at the University of California, Los Angeles. He has also served as the Boeing Professor at the University of Washington and as an adjunct professor at the University of California, San Diego. He is the author, editor, or co-author of more than 100 textbooks and handbooks and has published more than 200 technical papers. In addition, he has been a Guggenheim Fellow, Fulbright Research Scholar, IEEE Fellow, and a recipient of IEEE's Baker Prize Award and Barry Carlton Award.
From the Back Cover
Inspired by the structure of the human brain, artificial neural networks have found many applications due to their ability to solve cumbersome or intractable problems by learning from data. Neural networks can adapt to new environments by learning, and deal with information that is noisy. inconsistent, vague, or probabilistic. This volume of Neural Network Systems Techniques and Applications is devoted to Optimization Techniques, including systems structures and computional methods.
Coverage includes:
- A unified view of optimal learning
- Orthogonal transformation techniques
- Sequential constructiive techniques
- Fast back propagation algorithms
- Neural networks with nonstationary or dynamic outputs
- Applications to constraint satisfaction
- Unsupervised learning neural networks
- Optimum Cerebellar Model of Articulation Controller systems
- A new statistical theory of optimum neural learning
- The role of the Radial Basis Function in nonlinear dynamical systems
Practitioners, researchers, and students in industrial, manufacturing, mechanical, electrical, and computer engineering will find this volume a unique reference to a diverse array of methods for achieving optimization.
"About this title" may belong to another edition of this title.
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