Refining local weights and certainty factors using a fuzzy neural network

In this paper a novel approach of tuning knowledge representation parameters (KRP) in a fuzzy production rule (FPR) using a fuzzy neural network (FNN) is proposed. Two KRP will be considered, i.e., the local weight of each proposition in a conjunctive and a disjunctive fuzzy production rules and the certainty factor of the whole rule. These parameters will be used in FPR whose fuzzy terms could not be represented with a distinct membership function or a discrete fuzzy set due to the fact that the bases of these fuzzy sets cannot be clearly or easily defined or identified. The significance of this research is that the refined parameters will result in a more accurate drawn conclusion of a multilevel reasoning system by adjusting the degree of truth of the drawn consequent with the local weight. Furthermore, the time required to consult with domain experts to tune or refine these parameters is greatly reduced as a FNN could help knowledge engineers solve this refinement problem.