摘要
提出一种直接从样本数据中获取模糊规则的算法．模糊规则的隶属函数通过计算样本数据的方差与期望而得出，规则的抽取通过一个５层模糊神经网络实现，该算法包括两部分，第１部分确定出最佳规则；第２部分通过学习提高推理精度，通过仿真验证了该算法的有效性．
In this paper,a general method to obtain fuzzy rules directly from numerical data is proposed.The membership functions of antecedent part and consequuent part are determined by calculating the variance and expectation of the examples.The extraction of fuzzy rules is done by using a fuzzy neural network.The algorithm has two parts.The first part is to determine the optimal number of fuzzy rules.The second part is to improve the accuracy of the inference system.Through a simulation example,the effectiveness of the proposed algorithm is verified.
出处
《系统工程学报》
CSCD
1998年第1期57-65,共9页
Journal of Systems Engineering
基金
国家教委博士点专项基金
辽宁省博士科研启动基金
