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Integrating knowledge-based systems and artificial neural networks for engineering

Published online by Cambridge University Press:  27 February 2009

Nabil Kartam ,
Ian Flood  and
Tanit Tongthong
Nabil Kartam
Affiliation:
Assistant Professor, Department of Civil Engineering, University of Maryland, College Park, MD 20742, USA
Ian Flood
Affiliation:
Assistant Professor, Department of Civil Engineering, University of Maryland, College Park, MD 20742, USA
Tanit Tongthong
Affiliation:
Graduate Research Assistant, Department of Civil Engineering, University of Maryland, College Park, MD 20742, USA
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Abstract

The feasibility and relative merits of integrating knowledge-based systems (KBSs) and artificial neural networks (ANNs) for application to engineering problems are presented and evaluated. The strength of KBSs lies in their ability to represent human judgment and solve problems by providing explanations from and reasoning with heuristic knowledge. ANNs demonstrate problem solving characteristics not inherent in KBSs, including an ability to learn from example, develop a generalized solution applicable to a range of examples of the problem, and process information extremely rapidly. In this respect, KBSs and ANNs are complementary, rather than alternatives, and may be integrated into a system that exploits the advantages of both technologies. The scope of application and quality of solutions produced by such a hybrid extend beyond the boundaries of the individual technologies. This paper identifies and describes how KBSs and ANNs can be integrated, and provides an evaluation of the advantages that will accrue in engineering applications.

Keywords

Artificial Neural NetworksKnowledge-Based SystemsHybrid KBS-ANN Systems

Information

Type
Articles
Information
AI EDAM , Volume 9 , Issue 1 , January 1995 , pp. 13 - 22
Copyright
Copyright © Cambridge University Press 1995

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