A new method for weighted fuzzy interpolative reasoning based on weights-learning techniques
Shyi‐Ming Chen, Yu‐Chuan Chang · 2010
This paper presents a weighted fuzzy interpolative reasoning method for sparse fuzzy rule-based systems which allows the antecedent variables appearing in the fuzzy rules to 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 apply the proposed weighted fuzzy interpolative reasoning method and the proposed weights-learning algorithm to deal with 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 existing methods.