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Optimization techniques /

"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 to a unified view of optimal learning, orthogonal transformation techniques, sequ...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Leondes, Cornelius T.
Formato: Electrónico eBook
Idioma:Inglés
Publicado: San Diego : Academic Press, 1998.
Colección:Neural network systems, techniques, and applications ; v. 2.
Temas:
Acceso en línea:Texto completo

MARC

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245 1 0 |a Optimization techniques /  |c edited by Cornelius T. Leondes. 
260 |a San Diego :  |b Academic Press,  |c 1998. 
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490 1 |a Neural network systems, techniques, and applications ;  |v v. 2 
504 |a Includes bibliographical references and indexes. 
505 0 |a Optimal learning in artificial neural networks : a theoretical view / Monica Bianchini [and others] -- Orthogonal transformation techniques in the optimization of feedforward neural network systems / Partha Pratim Kanjilal -- Sequential constructive techniques / Marco Muselli -- Fast backpropagation training using optimal learning rate and momentum / Xiao-Hu Yu, Li-Qun Xu, and Yong Wang -- Learning of nonstationary processes / V. Ruiz de Angulo and Carme Torras -- Constraint satisfaction problems / Hans Nikolaus Schaller -- Dominant neuron techniques / Jar-Ferr Yang and Chi-Ming Chen --CMAC-based techniques for adaptive learning control / Chun-Shin Lin, Ching-Tsan Chiang, and Hyongsuk Kim -- Information dynamics and neural techniques for data analysis / Gustavo Deco -- Radial basis function network approximation and learning in task-dependent feedforward control of nonlinear dynamical systems / Dimitry Gorinevsky. 
588 0 |a Print version record. 
520 |a "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 to a 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. It provides in-depth treatment of theoretical contributions to optimal learning for neural network systems and offers a comprehensive treatment of orthogonal transformation techniques for the optimization of neural network systems.; It includes illustrative examples and comprehensive treatment of sequential constructive techniques for optimization of neural network systems and presents a uniquely comprehensive treatment of the highly effective fast back propagation algorithms for the optimization of neural network systems. It treats, in detail, optimization techniques for neural network systems with nonstationary or dynamic inputs. It covers optimization techniques and applications of neural network systems in constraint satisfaction 
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830 0 |a Neural network systems, techniques, and applications ;  |v v. 2. 
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