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Neural Networks and Qualitative Physics A Viability Approach [Paperback]

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  • Category: Books (Mathematics)
  • Author:  Aubin, Jean-Pierre
  • Author:  Aubin, Jean-Pierre
  • ISBN-10:  1107402840
  • ISBN-10:  1107402840
  • ISBN-13:  9781107402843
  • ISBN-13:  9781107402843
  • Publisher:  Cambridge University Press
  • Publisher:  Cambridge University Press
  • Pages:  302
  • Pages:  302
  • Binding:  Paperback
  • Binding:  Paperback
  • Pub Date:  01-May-2011
  • Pub Date:  01-May-2011
  • SKU:  1107402840-11-MPOD
  • SKU:  1107402840-11-MPOD
  • Item ID: 100841622
  • Seller: ShopSpell
  • Ships in: 2 business days
  • Transit time: Up to 5 business days
  • Delivery by: Jan 21 to Jan 23
  • Notes: Brand New Book. Order Now.
This book is devoted to some mathematical methods that arise in two domains of artificial intelligence: neural networks and qualitative physics.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, and set-valued analysis that plays a crucial role in qualitative analysis and simulation.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, and set-valued analysis that plays a crucial role in qualitative analysis and simulation.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.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 simulalÓ&
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