关键词: 模糊系统, 模拟退火算法, 支持度, 冗余规则, 可解释性

Abstract:

In order to solve the problem that the current fuzzy system modeling and optimization methods have not paid enough attention to the number of rules and the structural optimization of the learned fuzzy system, which affects its accuracy and interpretability, a fuzzy system optimization method that combines simulated annealing with support reduction rules is proposed. The method reduces the redundant rules of the system through support calculation and improves the interpretability of the fuzzy system; and uses simulated annealing algorithm to optimize the membership function parameters of the fuzzy system to improve the accuracy of the fuzzy system. For the regression problem, compared with BP（Back Propagation）, RBF（Radial Basis Function） algorithm and classic fuzzy algorithm WM（Wang-Mendel） on three classic data sets in different fields, the experimental results show that the proposed algorithm has achieved higher accuracy in prediction. Compared with WM algorithm, the number of algorithm rules proposed is significantly reduced.

Key words: fuzzy system, simulated annealing algorithm, degree of support, redundancy rule, interpretability