Weights-Learning for Weighted Fuzzy Rule Interpolation in Sparse Fuzzy Rule-Based Systems

In this paper, we present a weights-learning algorithm based on the CHC algorithm, which is a specialization of traditional genetic algorithms, to automatically learn the optimal weights of the antecedent variables of the fuzzy rules for the proposed weighted fuzzy interpolative reasoning method based on bell-shaped membership functions. We also apply the proposed method to deal with the truck backer-upper control problem. The experimental results show that the proposed method using the optimally learned weights gets better accuracy rates than the existing methods for dealing with the truck backer-upper control problem.