A neuro-fuzzy-GA system architecture for helping the knowledge acquisition process

Abstract

The knowledge acquisition process consists on extracting knowledge of a domain expert. This work aims to minimize the intrinsic difficulties of the knowledge acquisition process. For achieve this purpose, all possible rules from the domain expert and a set of example were obtained for a short time interval. The proposed hybrid expert system minimizes the knowledge acquisition difficulties using a new methodology. To build this hybrid architecture, several tools were used: symbolic paradigm, connectionist paradigm, fuzzy logic and genetic algorithm.