Radial Basis Function Networks 1

Lakhmi C. Jain

ISBN 10: 3790813672 / ISBN 13: 9783790813678

Verlag: Physica-Verlag HD Mär 2001, 2001

Beschreibung

Neuware -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.Physica Verlag, Tiergartenstr. 17, 69121 Heidelberg 340 pp. Englisch.

Bestandsnummer des Verkäufers 9783790813678

Bibliografische Details

• Titel: Radial Basis Function Networks 1
• Verlag: Physica-Verlag HD Mär 2001
• Erscheinungsjahr: 2001
• Sprache: Englisch
• ISBN-10: 3790813672
• ISBN-13: 9783790813678
• Einband: Buch
• Zustand: Neu

Über diesen Titel

Inhaltsangabe

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.