wolfskil at mit.edu
Fri Mar 3 14:45:55 EST 2000
The following is a book which readers of this list might find of
interest. For more information please visit
edited by Ian Cloete and Jacek M. Zurada
Neurocomputing methods are loosely based on a model of the brain as a
network of simple interconnected processing elements corresponding to
neurons. These methods derive their power from the collective processing
of artificial neurons, the chief advantage being that such systems can
learn and adapt to a changing environment. In knowledge-based
neurocomputing, the emphasis is on the use and representation of
knowledge about an application. Explicit modeling of the knowledge
represented by such a system remains a major research topic. The reason
is that humans find it difficult to interpret the numeric representation
of a neural network.
The key assumption of knowledge-based neurocomputing is that knowledge
is obtainable from, or can be represented by, a neurocomputing system in
a form that humans can understand. That is, the knowledge embedded in
the neurocomputing system can also be represented in a symbolic or
well-structured form, such as Boolean functions, automata, rules, or
other familiar ways. The focus of knowledge-based computing is on
methods to encode prior knowledge and to extract, refine, and revise
knowledge within a neurocomputing system.
Ian Cloete is Professor of Computer Science at the International
University in Germany in Bruchsal, Germany. Jacek M. Zurada is the S. T.
Fife Alumni Professor of Electrical Engineering at the University of
Louisville, Louisville, Kentucky, and the Editor-in-Chief of IEEE
Transactions on Neural Networks.
7 x 10, 500 pp., 209 illus., cloth ISBN 0-262-03274-0
More information about the Neur-sci