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.