In this paper, we use the concept of rough sets to define equivalence classes encoding input data, and to eliminate redundant or insignificant attributes in data sets which leads to reduction of the complexity of designed systems. In order to deal with ill-defined or real experimental data, we represent input object as fuzzy variables by fuzzy membership function. Furthermore we incorporate the significance factor of the input feature, corresponding to output pattern classification, in order to constitute a fuzzy inference which enhances classification considered as a nonlinear mapping. A new kind of rough fuzzy neural classifier and a learning algorithm with LSE are proposed in this paper. The neuro-fuzzy classifier proposed here can realize a nonlinear mapping from the input feature vector space (that may have the overlapping characteristic) into the output classification vector space.
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