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Neural Networks and Qualitative Physics

A Viability Approach

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Hardcover | 12 August 1996

At a Glance

Hardcover
302 Pages

Dimensions(cm)
22.86 x 15.24 x 2.06

Hardcover

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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.

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You Can Find This Book In

Non-Fiction Computing & I.T.Computer Science Artificial Intelligence Science Physics Mathematics Probability & Statistics Calculus & Mathematical Analysis Differential Calculus & Equations

Neural networks: a control approach
Pseudo-inverses and tensor products
Associative memories
The gradient method
Nonlinear neural networks
External learning algorithm of feedback controls
Internal learning algorithm of feedback controls
Learning processes of cognitive systems
Qualitative analysis of static problems
Dynamical qualitative simulation
Convex and nonsmooth analysis
Control of an AUV
Table of Contents provided by Publisher. All Rights Reserved.

Neural Networks and Qualitative Physics

A Viability Approach

At a Glance

Hardcover

Shipping

How to return your order

Defective items

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