Weighted fuzzy interpolative reasoning for sparse fuzzy rule-based systems

In this paper, we present a weighted fuzzy interpolative reasoning method for sparse fuzzy rule-based systems, where the antecedent variables appearing in the fuzzy rules have different weights. We also present a weights-learning algorithm to automatically learn the optimal weights of the antecedent variables of the fuzzy rules for the proposed weighted fuzzy interpolative reasoning method. We also apply the proposed weighted fuzzy interpolative reasoning method and the proposed weights-learning algorithm to handle the truck backer-upper control problem. The experimental results show that the proposed fuzzy interpolative reasoning method using the optimally learned weights by the proposed weights-learning algorithm gets better truck backer-upper control results than the ones by the traditional fuzzy inference system and the existing fuzzy interpolative reasoning methods. The proposed method provides us with a useful way for fuzzy rules interpolation in sparse fuzzy rule-based systems.