A Study of Neural and Fuzzy Parameters for Explicit and Implicit Knowledge-based Systems
نویسنده
چکیده
In this paper, a framework of a unified neural and neuro-fuzzy approach to integrate implicit and explicit knowledge in hybrid intelligent systems is presented. In the developed hybrid system, training data used for neural and neuro-fuzzy models represents implicit domain knowledge. On the other hand, the explicit domain knowledge is represented by fuzzy rules, directly mapped into equivalent connectionist structures. A formal model for a hybrid intelligent system implemented as neural, neuro-fuzzy and fuzzy modules is proposed. Furthermore, this paper explores the influences of the main identified parameters of the proposed model on the accuracy of the hybrid intelligent system in a predictive data mining application.
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