by Kevin Gurney
Covers: artificial neurons as models of their real counterparts; the geometry of network action in pattern space; gradient descent methods, including back-propagation; associative memory and Hopfield nets; and self-organization and feature maps.

by Christopher M. Bishop
The term pattern recognition encompasses a wide range of information processing problems of great practical significance, from speech recognition and the classification of handwritten characters,...

by Laurene V. Fausett
Features systematic discussions of all major neural nets. Author shows architectures in a similar format for all networks and illustrates the similarities and differences among them.

by Russell D. Reed, Robert J. Marks II
A practical book, Neural Smithing is aimed at the reader who intends to design and build neural networks for applications from forecasting to pattern recognition. The authors concentrate on multilayer perceptrons (MLPs) as the most commonly used neural network model, which adds to the book's overall clarity and focus.