Abstract. In order to achieve the non-stationary de-noising signal effectively, and to solve the prediction of less sample, a hybrid model composed of FCCA (Fuzzy C-means clustering algorithm) and FOM (Fractional Order Model) was constructed. The degree of each data point was determined by FCCA to de-noise and the order cumulative matrix was extended to fractional cumulative matrix, so that the fractional order cumulative grey model was established to make forecasting. The results of numerical example showed that the hybrid model can obtain better prediction accuracy.

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Abstract. Aiming at improving the accuracy of consumption prediction, a hybrid model was constructed, which designs an empirical wavelet filter bank to remove noise factors in original data. Besides the value prediction, the EWT-PGPR model can also give a certain credible interval, which effectively improves the practicability of the model.

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In order to effectively overcome the original evaluation method of subjectivity, the complexity of equipment maintenance, the uncertainty problem, based on the analysis of existing evaluation method, construct the evaluation index system of equipment maintenance support capability, illustrates the basic principle of two tuple, will evaluate the two tuple and capacity closely, given the equipment maintenance support capability two Yuan based on semantic evaluation method, and analyzes an example. Finally, we compare the two-tuple linguistic analysis with AHP and BP neural network. The results show that the method can objectively evaluate the equipment maintenance support ability and has certain reference value.