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论文类型：会议论文

发表时间：2008-01-01

收录刊物：CPCI-S

页面范围：590-595

关键字：Fuzzy neural network; Max-min fuzzy neural network; Error function; Lattice degree of nearness

摘要：The error function of a fuzzy neural network (FNN) is usually defined as the sum of the squares of the differences between the desired outputs and the actual outputs for all the patterns to be learnt. This error function can be viewed as "distance" nearness between two fuzzy sets, and is a type of similarity measure. In this paper, a new definition of the error function and a corresponding new training algorithm are proposed for max-min FNNs in terms of the lattice degree of nearness, which is a type of "shape" similarity measure between two fuzzy sets. As demonstrated by two numerical experiments, the new algorithm shows better convergence than the conventional one.