WEIGHTED FUZZY INTERPOLATIVE REASONING FOR SPARSE FUZZY RULE-BASED SYSTEMS BASED ON TRANSFORMATION TECHNIQUES

摘要

In this paper, we present a new weighted fuzzy interpolative reasoning method for sparse fuzzy rule-based systems. For multiple antecedent variables interpolation, the proposed method allows each condition appearing in the antecedent parts of fuzzy rules associated with a weighting factor. The a-cuts and transformation techniques are extended to handle the weighted fuzzy interpolative reasoning in sparse fuzzy rule-based systems. The proposed method provides us a useful way to deal with weighted fuzzy interpolative reasoning in sparse fuzzy rule-based systems.