Knowledge-Based Neurocomputing - Hardcover

Hardcover

ISBN 10: 0262032740 ISBN 13: 9780262032742

Publisher: Mit Pr, 2000

Synopsis

Looking at ways to encode prior knowledge and to extract, refine, and revise knowledge within a neurocomputing system.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.ContributorsC. 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

About this title

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.

Review:

Zurada's first volume is arguable the best neural network text ever written. Cloete and Zurada's Knowledge-Based Neurocomputing continues in this tradition of excellence. Clearly and precisely written, this volume belongs in the library of every neuro smith.―Robert J. Marks II, Department of Electrical Engineering, University of Washington, Seattle, and former Editor-in-Chief, IEEE Transaction on Neural Networks

About this edition

• Publisher: Mit Pr
• Publication date: 2000
• ISBN 10: 0262032740
• ISBN 13: 9780262032742
• Binding: Hardcover
• Number of pages: 500
• Editor: Cloete Ian, Zurada Jacek M.