MULTIPLE FUZZY RULES INTERPOLATION WITH WEIGHTED ANTECEDENT VARIABLES IN SPARSE FUZZY RULE-BASED SYSTEMS

Abstract

This paper presents a new method for multiple fuzzy rules interpolation with weighted antecedent variables in sparse fuzzy rule-based systems based on polygonal membership functions. First, the proposed method calculates the normalized weighting vector of each closest fuzzy rule. Then, it calculates the composite weight of each closest fuzzy rule. Then, it calculates the left normal point [Formula: see text] and the right normal point [Formula: see text] of the fuzzy interpolative reasoning result [Formula: see text], respectively. Finally, it calculates the characteristic points [Formula: see text] and [Formula: see text] of the fuzzy interpolative reasoning result B*, respectively. The experimental results show that the proposed method can generate more reasonable fuzzy interpolative reasoning results than the existing methods for sparse fuzzy rule-based systems. The proposed method can overcome the drawbacks of Chang etal.'s method (IEEE Trans. Fuzzy Syst.16(5) (2008) 1285–1301), Chen and Ko's method (IEEE Trans. Fuzzy Syst.16(6) (2008) 1626–1648) and Huang and Shen's method (IEEE Trans. Fuzzy Syst.14(2) (2006) 340–359) for multiple fuzzy rules interpolation. It provides us with a useful way for dealing with multiple fuzzy rules interpolation in sparse fuzzy rule-based systems.