Mathematical approaches to neural networks
Author(s)
Bibliographic Information
Mathematical approaches to neural networks
(North-Holland mathematical library, v. 51)
North-Holland, 1993
Available at 50 libraries
  Aomori
  Iwate
  Miyagi
  Akita
  Yamagata
  Fukushima
  Ibaraki
  Tochigi
  Gunma
  Saitama
  Chiba
  Tokyo
  Kanagawa
  Niigata
  Toyama
  Ishikawa
  Fukui
  Yamanashi
  Nagano
  Gifu
  Shizuoka
  Aichi
  Mie
  Shiga
  Kyoto
  Osaka
  Hyogo
  Nara
  Wakayama
  Tottori
  Shimane
  Okayama
  Hiroshima
  Yamaguchi
  Tokushima
  Kagawa
  Ehime
  Kochi
  Fukuoka
  Saga
  Nagasaki
  Kumamoto
  Oita
  Miyazaki
  Kagoshima
  Okinawa
  Korea
  China
  Thailand
  United Kingdom
  Germany
  Switzerland
  France
  Belgium
  Netherlands
  Sweden
  Norway
  United States of America
Note
Includes bibliographical references
Description and Table of Contents
Description
The subject of Neural Networks is coming of age, after its initial inception 50 years ago in the seminal work of McCulloch and Pitts. It is proving to be valuable in a wide range of academic disciplines and in important applications in industrial and business tasks. The progress being made in each approach is considerable. Nevertheless, both stand in need of a theoretical framework of explanation to underpin their usage and to allow the progress being made to be put on a firmer footing. This book aims to strengthen the foundations in its presentation of mathematical approaches to neural networks. It is through these that a suitable explanatory framework is expected to be found. The approaches span a broad range, from single neuron details to numerical analysis, functional analysis and dynamical systems theory. Each of these avenues provides its own insights into the way neural networks can be understood, both for artificial ones and simplified simulations. As a whole, the publication underlines the importance of the ever-deepening mathematical understanding of neural networks.
Table of Contents
Control theory approach (P.J. Antsaklis). Computational learning theory for artificial neural networks (M. Anthony, N. Biggs). Time-summating network approach (P.C. Bressloff). The numerical analysis approach (S.W. Ellacott). Self-organizing neural networks for stable control of autonomous behaviour in a changing world (S. Grossberg). On-line learning processes in artificial neural networks (T.M. Heskes, B. Kappen). Multilayer functionals (D.S. Modha, R. Hecht-Nielsen). Neural networks: the spin glass approach (D. Sherrington). Dynamics of attractor neural networks (T. Coolen, D. Sherrincton). Information theory and neural networks (J.G. Taylor, M.D. Plumbley). Mathematical analysis of a competitive network for attention (J.G. Taylor, F.N. Alavi).
by "Nielsen BookData"