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.
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