Synopses & Reviews
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.
Synopsis
Artificial intelligence covering neural networks, cognitive systems, qualitative physics.
Synopsis
This book is devoted to mathematical methods (control and viabilty theory, set-values analysis, etc.) arising in such domains of artificial intelligence as neural networks, cognitive systems and qualitative physics.
Description
Includes bibliographical references (p. 262-279) and index.