摘要
本文提出了一种模糊逻辑系统的快速学习算法 .算法要求预先确定各输入变量上模糊集合的数目及分布 ;模糊规则前件可以是任意形状的模糊集合 ,后件则必须采用单值模糊集合 ;模糊推理采用乘积推理 ;解模糊方法采用 Tsukamoto方法 .算法由输入 -输出数据对提取模糊规则 .模糊规则的后件采用最小二乘方法一次计算得出 .本算法对目标对象的逼近精度取决于输入参数上模糊集合的数目 ,数目越多 ,精度越高 .
In this paper a fast learning algorithm is presented for fuzzy logic system. The number and the distribution of fuzzy sets in input variables must be previously settled. The antecedent part of fuzzy rules could be any form of fuzzy sets and the consequent must be singleton form; Fuzzy inference adopts product method. Defuzzification is Tsukamoto method. Fuzzy rules are obtained from the input ouput data pairs. The value of fuzzy rule' consequent is calculated just one time by least square method. The precision that it could reach depends on the the number of fuzzy sets in input variables. This algorithms consumes realtively less computing power than other learning algorithms.
出处
《信息与控制》
CSCD
北大核心
2001年第6期555-557,共3页
Information and Control
基金
高等学校骨干教师计划资助项目(教技司 [2 0 0 0 ] )
上海市曙光计划资助项目 (合同号 990 68)
