Neural networks an introduction B. Müller ; J. Reinhardt / Physics of neural networks

Müller, Berndt (Verfasser) and Joachim (Verfasser) Reinhardt:

ISBN 10: 3540523804 / ISBN 13: 9783540523802

Edité par Berlin ; Heidelberg : Springer, 1990

A propos de cet article

XIII, 266 S. : 83 graph. Darst. ; 25 cm , 711g LEIDER OHNE FLOPPY-DISK, sonst sehr gut --- Flopyy disk is missing unfortunately, but the book is in very good condition Sprache: Englisch Gewicht in Gramm: 711.

N° de réf. du vendeur 49478

Détails bibliographiques

• Titre: Neural networks an introduction B. Müller ; J. Reinhardt / Physics of neural networks
• Éditeur: Berlin ; Heidelberg : Springer
• Date de parution: 1990
• Langue: anglais
• ISBN à 10 chiffres: 3540523804
• ISBN à 13 chiffres: 9783540523802
• Reliure: *geb.
• État de l'article: Sehr gut
• Poids: 711 grammes
• Catalogues du vendeur:
  • Physik

A propos de ce titre

Synopsis

The concepts of neural-network models and techniques of parallel distributed processing are comprehensively presented in a three-step approach. After a brief overview of the neural structure of the brain and the history of neural-network modelling, the reader is introduced to "neural" information processing, such as associative memory, perceptrons, feature-sensitive networks, learning strategies and practical applications. Part 2 covers more advanced subjects such as spin glasses, the mean-field theory of the Hopfield model, and the space of interactions in neural networks. The self-contained final part discusses seven programmes that provide practical demonstrations of neural-network models and their learning strategies. Software is included with the text on a 5 1/4-inch MS-DOS diskette and can be run using Borland's TURBO-C 2.0 compiler, the Microsoft C compiler (5.0), or compatible compilers.

Présentation de l'éditeur

Neural Networks presents concepts of neural-network models and techniques of parallel distributed processing in a three-step approach: - A brief overview of the neural structure of the brain and the history of neural-network modeling introduces to associative memory, preceptrons, feature-sensitive networks, learning strategies, and practical applications. - The second part covers subjects like statistical physics of spin glasses, the mean-field theory of the Hopfield model, and the "space of interactions" approach to the storage capacity of neural networks. - The final part discusses nine programs with practical demonstrations of neural-network models. The software and source code in C are on a 3 1/2" MS-DOS diskette can be run with Microsoft, Borland, Turbo-C, or compatible compilers.