Stochastic Models Of Neural Networks
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Stochastic Models of Neural Networks
This book is intended to provide a treatment of the theory and applications of Stochastic Neural Networks, that is networks able to learn random processes from experience, on the basis of recent developments on this subject. The mathematical frameworks on which the theory is founded embrace the approximation of non-random functions as well as the theory of stochastic processes. The networks so defined constitute an original and very promising model of human brain neural activity consistent with the need of learning from a stochastic environment. Moreover, the problem of speech modeling, both for synthesis and recognition, is faced as concrete and significant application in the field of artificial intelligence of the theory is presented.
Neural Networks
Author: Berndt Müller
language: en
Publisher: Springer Science & Business Media
Release Date: 2012-12-06
Neural Networks presents concepts of neural-network models and techniques of parallel distributed processing in a three-step approach: - A brief overview of the neural structure of the brain and the history of neural-network modeling introduces to associative memory, preceptrons, feature-sensitive networks, learning strategies, and practical applications. - The second part covers subjects like statistical physics of spin glasses, the mean-field theory of the Hopfield model, and the "space of interactions" approach to the storage capacity of neural networks. - The final part discusses nine programs with practical demonstrations of neural-network models. The software and source code in C are on a 3 1/2" MS-DOS diskette can be run with Microsoft, Borland, Turbo-C, or compatible compilers.
Advanced Models of Neural Networks
This book provides a complete study on neural structures exhibiting nonlinear and stochastic dynamics, elaborating on neural dynamics by introducing advanced models of neural networks. It overviews the main findings in the modelling of neural dynamics in terms of electrical circuits and examines their stability properties with the use of dynamical systems theory. It is suitable for researchers and postgraduate students engaged with neural networks and dynamical systems theory.