Radial Basis Function Networks 1 (Hardcover)

Robert J.Howlett

ISBN 10: 3790813672 / ISBN 13: 9783790813678

Published by Springer-Verlag Berlin and Heidelberg GmbH & Co. KG, Heidelberg, 2001

About this Item

Hardcover. The Radial Basis Function (RBF) neural network has gained in popularity over recent years because of its rapid training and its desirable properties in classification and functional approximation applications. RBF network research has focused on enhanced training algorithms and variations on the basic architecture to improve the performance of the network. In addition, the RBF network is proving to be a valuable tool in a diverse range of application areas, for example, robotics, biomedical engineering, and the financial sector. The two volumes provide a comprehensive survey of the latest developments in this area. Volume 1 covers advances in training algorithms, variations on the architecture and function of the basis neurons, and hybrid paradigms, for example RBF learning using genetic algorithms. Both volumes will prove extremely useful to practitioners in the field, engineers, researchers and technically accomplished managers. The Radial Basis Function (RBF) neural network has gained in popularity over recent years because of its rapid training and its desirable properties in classification and functional approximation applications. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Seller Inventory # 9783790813678

Bibliographic Details

• Title: Radial Basis Function Networks 1 (Hardcover)
• Publisher: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG, Heidelberg
• Publication Date: 2001
• Language: English
• ISBN 10: 3790813672
• ISBN 13: 9783790813678
• Binding: Hardcover
• Condition: new

About this title

Synopsis

The Radial Basis Function (RBF) neural network has gained in popularity over recent years because of its rapid training and its desirable properties in classification and functional approximation applications. RBF network research has focused on enhanced training algorithms and variations on the basic architecture to improve the performance of the network. In addition, the RBF network is proving to be a valuable tool in a diverse range of application areas, for example, robotics, biomedical engineering, and the financial sector. The two volumes provide a comprehensive survey of the latest developments in this area. Volume 1 covers advances in training algorithms, variations on the architecture and function of the basis neurons, and hybrid paradigms, for example RBF learning using genetic algorithms. Both volumes will prove extremely useful to practitioners in the field, engineers, researchers and technically accomplished managers.