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.
Presents a taxonomy and reviews of methods for obtaining comprehensible neural network topologies by learning, as well as the encoding, extraction and refinement of symbolic knowledge within a neural network. DLC: Neural computers.
About the Author
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.