Review
"A significant and scholarly contribution to the field of neural networks. The author successfully integrates a very broad range of material in a competent and understandable manner. I do not know of any book on this same topic that covers such a wide range of material." --Jerome R. Busemeyer, Professor of Psychology, Purdue University
Product Description
This graduate-level text teaches students how to use a small number of powerful mathematical tools for analyzing and designing a wide variety of artificial neural network (ANN) systems, including their own customized neural networks. "Mathematical Methods for Neural Network Analysis and Design" offers a broad and integrated approach that explains each tool in a manner that is independent of specific ANN systems. Included are chapter summaries and detailed solutions to over 100 ANN system analysis and design problems. For convienence, many of the proofs of the key theorems have been rewritten so that the entire book uses a relatively uniform notion. This text is organized according to categories of mathematical tools - for investigating the behaviour of an ANN system, for comparing (and improving) the efficiency of system computations, and for evaluating its computational goals - that correspond respectively to David Marr's implementational, algorithmic and computational levels of description. And instead of devoting separate chapters to different types of ANN system, it analyzes the same group of ANN systems from the perspective of different mathematical methodologies.
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