Radial Basis Function Networks 2: New Advances in Design. Studies in Fuzziness and Soft Computing: Volume 67

Robert J. Howlett and Lakhmi C. Jain, Editors

ISBN 10: 3790813680 / ISBN 13: 9783790813685

Editore: Physica-Verlag, 2001

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Physica-Verlag, Heidelberg, copyright 2001. xix, 360 pages. Illustrated. 9.5 x 6.5", hardcover, no dj. No dj, clean, tight, very good/none.

Codice articolo 95519

Dati bibliografici

• Titolo: Radial Basis Function Networks 2: New Advances in Design. Studies in Fuzziness and Soft Computing: Volume 67
• Editore: Physica-Verlag
• Data di pubblicazione: 2001
• Lingua: Inglese
• ISBN 10: 3790813680
• ISBN 13: 9783790813685
• Rilegatura: Hardcover
• Condizione: Used - Very Good

Descrizione articolo

Riassunto

The Radial Basis Function (RBF) network has gained in popularity in recent years. This is due to its desirable properties in classification and functional approximation applications, accompanied by training that is more rapid than that of many other neural-network techniques. 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 applications areas, for example, robotics, biomedical engineering, and the financial sector. The two-title series Theory and Applications of Radial Basis Function Networks provides a comprehensive survey of recent RBF network research. This volume, New Advances in Design, contains a wide range of applications in the laboratory and case-studies describing current use. The sister volume to this one, Recent Developments in Theory and Applications, covers advances in training algorithms, variations on the architecture and function of the basis neurons, and hybrid paradigms. The combination of the two volumes will prove extremely useful to practitioners in the field, engineers, researchers, students and technically accomplished managers.

Dalla quarta di copertina

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 2 contains a wide range of applications in the laboratory and case studies describing current industrial use. Both volumes will prove extremely useful to practitioners in the field, engineers, reserachers, students and technically accomplished managers.