MTU Cork Library Catalogue

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Neural networks and qualitative physics / Jean-Pierre Aubin.

By: Aubin, Jean Pierre.
Material type: materialTypeLabelBookPublisher: Cambridge. New York : Cambridge University Press, 1996Description: xvii, 283 p. : ill. ; 24 cm.ISBN: 0521445329 .Subject(s): Artificial intelligence -- Mathematics | Neural networks (Computer science) | Mathematical physicsDDC classification: 006.3
Contents:
Neural networks: A control approach -- Pseudoinverses and tensor products -- Associate memories -- The gradient method -- Nonlinear neural networks -- External learning algorithm for feedback controls -- Learning processes of cognitive systems -- Qualitative analysis of static problems -- Dynamical qualitative simulation.
Holdings
Item type Current library Call number Copy number Status Date due Barcode Item holds
General Lending MTU Bishopstown Library Lending 006.3 (Browse shelf(Opens below)) 1 Available 00014469
Total holds: 0

Enhanced descriptions from Syndetics:

This book is devoted to some mathematical methods that arise in two domains of artificial intelligence: neural networks and qualitative physics. Professor Aubin makes use of control and viability theory in neural networks and cognitive systems, regarded as dynamical systems controlled by synaptic matrices, and set-valued analysis that plays a natural and crucial role in qualitative analysis and simulation. This allows many examples of neural networks to be presented in a unified way. In addition, several results on the control of linear and nonlinear systems are used to obtain a "learning algorithm" of pattern classification problems, such as the back-propagation formula, as well as learning algorithms of feedback regulation laws of solutions to control systems subject to state constraints.

Bibliography: (pages 262-279) and index.

Neural networks: A control approach -- Pseudoinverses and tensor products -- Associate memories -- The gradient method -- Nonlinear neural networks -- External learning algorithm for feedback controls -- Learning processes of cognitive systems -- Qualitative analysis of static problems -- Dynamical qualitative simulation.

Table of contents provided by Syndetics

  • 1 Neural networks: a control approach
  • 2 Pseudo-inverses and tensor products
  • 3 Associative memories
  • 4 The gradient method
  • 5 Nonlinear neural networks
  • 6 External learning algorithm of feedback controls
  • 7 Internal learning algorithm of feedback controls
  • 8 Learning processes of cognitive systems
  • 9 Qualitative analysis of static problems
  • 10 Dynamical qualitative simulation
  • Appendix A Convex and nonsmooth analysis
  • Appendix B Control of an AUV

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