This article used the Cluster analysis of statistical method to separate China＇s 30 provinces and municipalities into three categories according to their energy consumption discrepancies and characteristics from 1985...This article used the Cluster analysis of statistical method to separate China＇s 30 provinces and municipalities into three categories according to their energy consumption discrepancies and characteristics from 1985 to 2007. The categories were high, moderate and low energy consumption areas and they had significant differences in energy consumption. Based on this classification, the authors analyzed the influencing factors of energy consumption in the three areas by means of panel data econometric model. The results showed that the influencing factors were obviously different. In order to support national goal of energy conservation and emission reduction, the energy measures and policies should be distinctly taken.展开更多
A novel approach for constructing robust Mamdani fuzzy system was proposed, which consisted of an efficiency robust estimator(partial robust M-regression, PRM) in the parameter learning phase of the initial fuzzy syst...A novel approach for constructing robust Mamdani fuzzy system was proposed, which consisted of an efficiency robust estimator(partial robust M-regression, PRM) in the parameter learning phase of the initial fuzzy system, and an improved subtractive clustering algorithm in the fuzzy-rule-selecting phase. The weights obtained in PRM, which gives protection against noise and outliers, were incorporated into the potential measure of the subtractive cluster algorithm to enhance the robustness of the fuzzy rule cluster process, and a compact Mamdani-type fuzzy system was established after the parameters in the consequent parts of rules were re-estimated by partial least squares(PLS). The main characteristics of the new approach were its simplicity and ability to construct fuzzy system fast and robustly. Simulation and experiment results show that the proposed approach can achieve satisfactory results in various kinds of data domains with noise and outliers. Compared with D-SVD and ARRBFN, the proposed approach yields much fewer rules and less RMSE values.展开更多
文摘This article used the Cluster analysis of statistical method to separate China＇s 30 provinces and municipalities into three categories according to their energy consumption discrepancies and characteristics from 1985 to 2007. The categories were high, moderate and low energy consumption areas and they had significant differences in energy consumption. Based on this classification, the authors analyzed the influencing factors of energy consumption in the three areas by means of panel data econometric model. The results showed that the influencing factors were obviously different. In order to support national goal of energy conservation and emission reduction, the energy measures and policies should be distinctly taken.
基金Project(61473298)supported by the National Natural Science Foundation of ChinaProject(2015QNA65)supported by Fundamental Research Funds for the Central Universities,China
文摘A novel approach for constructing robust Mamdani fuzzy system was proposed, which consisted of an efficiency robust estimator(partial robust M-regression, PRM) in the parameter learning phase of the initial fuzzy system, and an improved subtractive clustering algorithm in the fuzzy-rule-selecting phase. The weights obtained in PRM, which gives protection against noise and outliers, were incorporated into the potential measure of the subtractive cluster algorithm to enhance the robustness of the fuzzy rule cluster process, and a compact Mamdani-type fuzzy system was established after the parameters in the consequent parts of rules were re-estimated by partial least squares(PLS). The main characteristics of the new approach were its simplicity and ability to construct fuzzy system fast and robustly. Simulation and experiment results show that the proposed approach can achieve satisfactory results in various kinds of data domains with noise and outliers. Compared with D-SVD and ARRBFN, the proposed approach yields much fewer rules and less RMSE values.
