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Providing a comprehensive survey of developments in the area of RBF networks, this volume 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. Editor(s): Howlett, Robert J.; Jain, Prof. Lakhmi C. Series: Studies in Fuzziness and Soft Computing. Num Pages: 318 pages, 14 black & white tables, biography. BIC Classification: UYQP. Category: (P) Professional & Vocational; (UP) Postgraduate, Research & Scholarly; (UU) Undergraduate. Dimension: 234 x 156 x 20. Weight in Grams: 653. . 2001. Hardback. . . . . Seller Inventory # V9783790813678
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.
From the Back Cover: 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.
Title: Radial Basis Function Networks
Publisher: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
Publication Date: 2001
Binding: Hardcover
Condition: New
Seller: Buchpark, Trebbin, Germany
Condition: Sehr gut. Zustand: Sehr gut | Sprache: Englisch | Produktart: Bücher. Seller Inventory # 695528/2
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Seller: moluna, Greven, Germany
Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Contains a wide range of applications in the laboratory and case-studies describing current use Overall view of the methods used for the genetic optimisation of artificial neural networks and presentation of the inherent problemsThe Radial Basis Fun. Seller Inventory # 5310329
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Seller: GreatBookPricesUK, Woodford Green, United Kingdom
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Seller: Ria Christie Collections, Uxbridge, United Kingdom
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Seller: Grand Eagle Retail, Bensenville, IL, U.S.A.
Hardcover. Condition: new. 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
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Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Buch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - 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. 318 pp. Englisch. Seller Inventory # 9783790813678
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Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
Buch. Condition: Neu. 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. Seller Inventory # 9783790813678
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Seller: AHA-BUCH GmbH, Einbeck, Germany
Buch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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. Seller Inventory # 9783790813678
Quantity: 1 available