Neural Networks and Qualitative Physics: A Viability Approach
Author:   Jean-Pierre Aubin (Université de Paris IX (Paris-Dauphine))
Publisher:   Cambridge University Press
ISBN:  

9780511626258


Publication Date:   05 August 2012
Format:   Undefined
Availability:   Available To Order   Availability explained
We have confirmation that this item is in stock with the supplier. It will be ordered in for you and dispatched immediately.

Our Price $594.00 Quantity:  
Add to Cart

Share |

Neural Networks and Qualitative Physics: A Viability Approach


Add your own review!

Overview

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. This book will be of value to anyone with an interest in neural networks and cognitive systems.

Full Product Details

Author:   Jean-Pierre Aubin (Université de Paris IX (Paris-Dauphine))
Publisher:   Cambridge University Press
Imprint:   Cambridge University Press (Virtual Publishing)
ISBN:  

9780511626258


ISBN 10:   0511626258
Publication Date:   05 August 2012
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Undefined
Publisher's Status:   Active
Availability:   Available To Order   Availability explained
We have confirmation that this item is in stock with the supplier. It will be ordered in for you and dispatched immediately.

Table of Contents

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.

Reviews

Author Information

Tab Content 6

Author Website:  

Customer Reviews

Recent Reviews

No review item found!

Add your own review!

Countries Available

All regions
Latest Reading Guide

MRG2025CC

 

Shopping Cart
Your cart is empty
 Shopping cart
Mailing List

 

Sign up now