在王国俊教授提出的满足还原性的单一规则的全蕴涵三I算法基础上,利用模糊集合的相似度给每条模糊推理规则赋予权重,即wi=1-S2,i■I1+S2,i∈I,其中S={max S1,S2,…,Sn}且I=i|Si=S,{1≤i≤n},使得全蕴涵三I算法在一般的模糊推理情形下也满足还原性。最后通过实例表明:用新方法推理得出的结果比文献[4]的全蕴涵三I算法的结果更具合理性。 Based on the all-implication triple-I algorithm which is proposed by Professor Wang Guojun and satisfies the reductive single rules, each fuzzy reasoning rule is weighted by the similarity of the fuzzy sets, that is wi = 1-S2, i ■ I1 + S2, i ∈I, where S = {max S1, S2, ..., Sn} and I = i | Si = S, {1≤i≤n}, the full implication triple I algorithm satisfies the reducibility under the general fuzzy reasoning . Finally, an example is given to show that the reasoning result obtained by the new method is more reasonable than the result of the full implication triple I method in [4].