Structural Dynamic Systems Computational Techniques and Optimization: Optimization Techniques (Gordon and Breadh International Series on Engineering, Technology and Applied Science)

ISBN 10: 9056996444 / ISBN 13: 9789056996444

Published by CRC Press, 1999

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Bibliographic Details

• Title: Structural Dynamic Systems Computational Techniques and Optimization: Optimization Techniques (Gordon and Breadh International Series on Engineering, Technology and Applied Science)
• Publisher: CRC Press
• Publication Date: 1999
• Language: English
• ISBN 10: 9056996444
• ISBN 13: 9789056996444
• Binding: Hardcover
• Condition: Like New
• Book Type: book

About this title

Synopsis

Formulation of an optimal dynamic structural system design problem requires identification of design variables that describe the structural system, a cost function that needs to be minimized, and performance and safety constraints for the system. The formulation of the problem depends upon the type of application and objectives to be achieved, i.e., the shape, the sizing, or topology design problem. Specific design variable definition, cost of function and constraints are dictated by the application. This volume is a comprehensive treatment of the general methods involved in this broadly fundamental problem and provides essential techniques in specific but pervasive structural dynamic systems elements and their optimization.

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.