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Based on the theory of multiresolution analysis of wavelet transforms and fuzzy concepts, a new method called fuzzy wavelet support vector machines (FWSVM) was presented. The FWSVM consists of a set of fuzzy rules. Each rules corresponding to a sub-wavelet support vector machines (WSVM) with different resolution. Thus the sub-WSVM at different dilation value under these fuzzy rules is fully utilized to capture various essential components of the system. The role of the fuzzy set is to determine the contribution of the sub-WSVM to the output of the FWSVM. Through adjusting the parameters of membership functions, the model accuracy and the generalization capability of the FWSVM can be improved. Analysis of the experimental results proved that FWSVM could achieve greater accuracy than the standard SVM.