Template-Based Fuzzy Systems Modeling

Article type: Research Article

Authors: Yager, Ronald R. | Filev, Dimitar P.

Affiliations: Machine Intelligence Institute, Iona College, New Rochelle, New York 10801

Abstract: A methodology is suggested here for the development of fuzzy systems models that is a combination of the AI-expert systems approach, with its heavy dependence on the use of expert knowledge, and the neural network-type systems building, with its emphasis on learning from data observations. We use expert-provided information in the form of template linguistic values to induce potential elemental rules for the knowledge base of the system model. We then introduce input-output observations into a simple learning mechanism to obtain weights characterizing the effect of each of the potential elemental rules on the overall systems model. The development of the learning mechanism is based on a representation of systems models combining fuzzy logic and Dempster-Shafer theory, which we previously introduced.

DOI: 10.3233/IFS-1994-2103

Journal: Journal of Intelligent and Fuzzy Systems, vol. 2, no. 1, pp. 39-54, 1994