Neural Networks

Neural Networks : Theory and Applications

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Description

Neural networks have attracted the interest of scientists from many disciplines: engineering, computer science, mathematics, physics, biology, and cognitive science. This volume collects 15 contributions, written by leading international researchers in these areas, that illustrate important features of various neural network methodologies. Two papers discuss fundamental principles of neural networks. The remainder detail modifications of basic neural systems that improve system performance in specific application domains. Where appropriate, improvements are demonstrated by numerical examples.
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Product details

  • Hardback | 376 pages
  • 159 x 220 x 22mm | 685g
  • Academic Press Inc
  • San Diego, United States
  • English
  • index
  • 0124670504
  • 9780124670501

Table of contents

Weightless neural tools - toward cognitive macrostructures, L. Aleksander; an estimation theoretic basis for the design of sorting and classification network, R.W. Brockett; a self organizing ARTMAP neural architecture for supervized learning and pattern recognition, G.A. Carpenter et al; hybrid neural network architectures - equilibrium systems that pay attention, L.N. Cooper; neural networks for internal representation of movements in primates and robots, R. Eckmiller et al; recognition and segmentation of characters in handwriting with selective attention, K. Fukushima et al; adaptive acquisition of language, A.L. Gorin et al; what connectionist models learn - learning and representation in connectionist networks, S.J. Hanson and D.J. Burr; early vision, focal attention and neural nets, B. Julesz; toward hierarchical matched filtering, R. Hecht-Nielsen; some variations on training of recurrent networks, G.M. Kuhn and N.P. Herzberg; generalized perception networks with nonlinear discriminant functions, S.Y. Kung et al; neural tree networks, A. Sankar and R. Mammone; capabilities and training of feedforward nets, E.D. Sontag; a fast learning algorithm for multilayer neural network based on projection methods, S.J. Yeh and H. Stark.
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