【摘要】 提出了一种二层学习算法来优化模糊规则基。利用TakagiSugeno模糊神经网络对一个模糊规则基进行参数学习,学习方法为梯度下降法,然后利用遗传算法对规则基进行结构调整,采用二进制编码方法,一条规则对应于一个基因位,一个规则基对应于一条染色体。这种二层优化方法能较好地减少模糊规则基的冗余度,化简模糊规则基。仿真实验也证实了这一点更多还原

【Abstract】 This paper proposes a hierarchical learning algorithm for optimizing fuzzy rule bases. In this algorithm, Takagi Sugeno fuzzy neural network is used for the parametric learning of a fuzzy rule base with steepest descent method. Then, the structure of fuzzy rule bases is optimized with the binary coding method, in which a rule corresponds to a gene bit, and a rule base to a chromosome. This hierarchical learning algorithm can reduce the redundant rules and simplify the fuzzy rule base. A computer simulation verifies the effectiveness of the proposed algorithm.更多还原