Radial Basis Function Networks 1 (Hardcover)

Robert J.Howlett

ISBN 10: 3790813672 / ISBN 13: 9783790813678

Edité par Springer-Verlag Berlin and Heidelberg GmbH & Co. KG, Heidelberg, 2001

A propos de cet article

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 our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

N° de réf. du vendeur 9783790813678

Détails bibliographiques

• Titre: Radial Basis Function Networks 1 (Hardcover)
• Éditeur: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG, Heidelberg
• Date de parution: 2001
• Langue: anglais
• ISBN à 10 chiffres: 3790813672
• ISBN à 13 chiffres: 9783790813678
• Reliure: Hardcover
• État de l'article: new

A propos de ce titre

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.