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Optimization Techniques (Neural Network Systems Techniques and Applications) (Neural Network Systems Techniques and Applications)
By Cornelius T. Leondes
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
Summary: A Reference Series for those who create & optimize NN's.
Rating: 4
Neural networks have seen an explosion in interest and application in the last 10 to 15 years, when they evolved from research on artificial intelligence One can find books on diverse subjects such as finance, medicine, and any physical or theoretical science with significant sections devoted to the use of neural networks in that discipline. I conducted a non-scientific survey (in 1 minute or less) of the importance of the subject matter by asking Amazon.com how many books it listed on subjects I thought might be equal in timing and importance. The results (below) imply a significant interest in neural networks, from readers and authors alike. My list does not report on the number of books that contain chapters or significant sections on neural networks.
·Neural Networks=1021 books listed; DNA=948 books; Enzymes=779 books, Genome=232 books, and Human Genome=100 books
Optimization Techniques is the second in a seven (7) volume series from Academic Press on neural network systems techniques and applications. The series presents itself as the first all-inclusive treatment of the subject matter and is aimed at a wide array of potential readers: researchers, students and practitioners in industrial, mechanical, electrical, manufacturing and computer engineering. As such, one would expect the series to be appealing to a more select audience of research workers focused on creating and improving neural networks, and not so much to those of us who use the applications and interpret the output. This seems to be the case.
This Volume in the series, claiming to be the first comprehensive treatment of optimization techniques including system structure and computational methods, presents the work of nineteen (19) contributors as a synthesis of what is known about neural networks and optimization techniques at the present time. The book is divided into ten (10) sections, each addressing different topic areas. I would not suspect that more than one or two sections would be of interest to the reader in an applied research field. I found the sections on the learning of nonstationary processes and neural techniques for data analysis to be informative and well written. I did not anticipate having a warm feeling of confidence in my level of understanding the first time I read these sections. I am confident, however, that I know which direction current and future research will take on neural networks.
Code
http://ifile.it/4niwb5a/0124438628.zip
http://rapidshare.com/files/154524549/0124438628.zip
http://www.filefactory.com/file/cd4199/n/0124438628_zip
http://rapidshare.com/files/154639532/0124438628.zip
http://www.megaupload.com/?d=6FGWW21Z
http://www.filefactory.com/file/6e86ee/n/0124438628_zip
By Cornelius T. Leondes
- Publisher: Academic Press
- Number Of Pages: 398
- Publication Date: 1998-01-15
- ISBN-10 / ASIN: 0124438628
- ISBN-13 / EAN: 9780124438620
- Binding: Hardcover
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
Summary: A Reference Series for those who create & optimize NN's.
Rating: 4
Neural networks have seen an explosion in interest and application in the last 10 to 15 years, when they evolved from research on artificial intelligence One can find books on diverse subjects such as finance, medicine, and any physical or theoretical science with significant sections devoted to the use of neural networks in that discipline. I conducted a non-scientific survey (in 1 minute or less) of the importance of the subject matter by asking Amazon.com how many books it listed on subjects I thought might be equal in timing and importance. The results (below) imply a significant interest in neural networks, from readers and authors alike. My list does not report on the number of books that contain chapters or significant sections on neural networks.
·Neural Networks=1021 books listed; DNA=948 books; Enzymes=779 books, Genome=232 books, and Human Genome=100 books
Optimization Techniques is the second in a seven (7) volume series from Academic Press on neural network systems techniques and applications. The series presents itself as the first all-inclusive treatment of the subject matter and is aimed at a wide array of potential readers: researchers, students and practitioners in industrial, mechanical, electrical, manufacturing and computer engineering. As such, one would expect the series to be appealing to a more select audience of research workers focused on creating and improving neural networks, and not so much to those of us who use the applications and interpret the output. This seems to be the case.
This Volume in the series, claiming to be the first comprehensive treatment of optimization techniques including system structure and computational methods, presents the work of nineteen (19) contributors as a synthesis of what is known about neural networks and optimization techniques at the present time. The book is divided into ten (10) sections, each addressing different topic areas. I would not suspect that more than one or two sections would be of interest to the reader in an applied research field. I found the sections on the learning of nonstationary processes and neural techniques for data analysis to be informative and well written. I did not anticipate having a warm feeling of confidence in my level of understanding the first time I read these sections. I am confident, however, that I know which direction current and future research will take on neural networks.
Code
http://ifile.it/4niwb5a/0124438628.zip
http://rapidshare.com/files/154524549/0124438628.zip
http://www.filefactory.com/file/cd4199/n/0124438628_zip
http://rapidshare.com/files/154639532/0124438628.zip
http://www.megaupload.com/?d=6FGWW21Z
http://www.filefactory.com/file/6e86ee/n/0124438628_zip
