Knowledge-based Neurocomputing

Front Cover
Ian Cloete, Jacek M. Zurada
MIT Press, 2000 - Computers - 486 pages
0 Reviews

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.

Contributors: C. Aldrich, J. Cervenka, I. Cloete, R.A. Cozzio, R. Drossu, J. Fletcher, C.L. Giles, F.S. Gouws, M. Hilario, M. Ishikawa, A. Lozowski, Z. Obradovic, C.W. Omlin, M. Riedmiller, P. Romero, G.P.J. Schmitz, J. Sima, A. Sperduti, M. Spott, J. Weisbrod, J.M. Zurada.

 

What people are saying - Write a review

We haven't found any reviews in the usual places.

Contents

Architectures and Techniques for KnowledgeBased
27
Symbolic Knowledge Representation in Recurrent Neural
63
A Tutorial on Neurocomputing of Structures
117
Structural Learning and Rule Discovery
153
Transformation of Rules to Artificial Neural
207
Integration of Heterogeneous Sources of Partial Domain
217
Approximation of Differential Equations Using Neural Networks
251
A Hybrid Architecture for SelfLearning Control
291
Data Mining Techniques for Designing Neural Network Time
325
Extraction of Decision Trees from Artificial Neural Networks
369
Extraction of Linguistic Rules from Data via Neural Networks
403
Neural Knowledge Processing in Expert Systems
419
Index
467
Copyright

Other editions - View all

Common terms and phrases

References to this book

All Book Search results »

About the author (2000)

Cloete is Professor of Computer Science at the International University in Germany in Bruchsal, Germany.

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 Transcations on Neural Networks.

Bibliographic information