Structural Dynamic Systems Com: Computer Aided Design and Engineering (Engineering Technology and Applied Science , Vol 7)

ISBN 10: 9056996428 / ISBN 13: 9789056996420

Published by Routledge, 1998

About this Item

Most items will be dispatched the same or the next working day. A copy that has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged. Ex library copy with usual stamps & stickers.

Seller Inventory # wbb0023158028

Bibliographic Details

• Title: Structural Dynamic Systems Com: Computer Aided Design and Engineering (Engineering Technology and Applied Science , Vol 7)
• Publisher: Routledge
• Publication Date: 1998
• Language: English
• ISBN 10: 9056996428
• ISBN 13: 9789056996420
• Binding: Hardcover
• Condition: Very Good
• Weight: 580.6 grams

About this title

Synopsis

The availability of powerful computers along with highly effective computational techniques have allowed computer-aided design and engineering of structural dynamics systems to achieve a high level of capability and importance. This volume clearly reveals the great significance of these techniques and the essential role they will play in the future as further development occurs. This will be a significant and unique reference for students, research workers, practitioners, computer scientists and others for years to come.

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.