An introduction to neural networks
UCL Press, 1 edition, (Aug 5, 1997)Abstract
Though mathematical ideas underpin the study of neural
networks, the author presents the fundamentals without
the full mathematical apparatus. All aspects of the
field are tackled, including 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. The traditionally difficult topic of adaptive
resonance theory is clarified within a hierarchical
description of its operation. The book also includes
several real-world examples to provide a concrete
focus. This should enhance its appeal to those involved
in the design, construction and management of networks
in commercial environments and who wish to improve
their understanding of network simulator packages. As a
comprehensive and highly accessible introduction to one
of the most important topics in cognitive and computer
science, this volume should interest a wide range of
readers, both students and professionals, in cognitive
science, psychology, computer science and electrical
engineering.
