Neural Network Models

Providing an in-depth treatment of neural network models, this volume explains and proves the main results in a clear and accessible way. It presents the essential principles of nonlinear dynamics as derived from neurobiology, and investigates the stability, convergence behaviour and capacity of networks. Also included are sections on stochastic networks and simulated annealing, presented using Markov processes rather than statistical physics, and a chapter on backpropagation. Each chapter ends with a suggested project designed to help the reader develop an integrated knowledge of the theory, placing it within a practical application domain. Neural Network Models: Theory and Projects concentrates on the essential parameters and results that will enable the reader to design hardware or software implementations of neural networks and to assess critically existing commercial products.

Providing an in-depth treatment of neural network models, this volume explains and proves the main results in a clear and accessible way. Topics covered include key concepts in neural networks, backpropagation, neurons in models of the brain, synchronous and discrete networks, differential mathematics, linear capacity, capacity from a signal to noise ratio, and neural networks and Markov chains. Each chapter ends with a suggested project designed to help the reader develop an integrated knowledge of the theory, placing it within a practical application domain. Neural Network Models: Theory and Projects concentrates on the essential parameters and results that will enable the reader to design hardware or software implementations of neural networks and to assess critically existing commercial products. It is suitable for final year, postgraduate and doctoral students in engineering, computing, applied mathematics, physics and biomedical systems, and will also be of interest to those working in science and industry who wish to obtain a firm grounding in the subject.