Robust partial leastsquares algorithm was carried out on determining distillation ranges and octane number of reformed gasoline by NearInfrared Spectroscopy(NIR).The computing standard error of the initial boiling...Robust partial leastsquares algorithm was carried out on determining distillation ranges and octane number of reformed gasoline by NearInfrared Spectroscopy(NIR).The computing standard error of the initial boiling point was 00349,while those of the other distillation ranges and octane number were better than that of IBP,and the standard error of 50 percent distillation range arrived at 00055.RPLS is rapid and robust,and has good accuracy and repeatability,and it will be an effective method in correcting distillation ranges and octane number of reformed gasoline.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Robust partial leastsquares algorithm was carried out on determining distillation ranges and octane number of reformed gasoline by NearInfrared Spectroscopy(NIR).The computing standard error of the initial boiling point was 00349,while those of the other distillation ranges and octane number were better than that of IBP,and the standard error of 50 percent distillation range arrived at 00055.RPLS is rapid and robust,and has good accuracy and repeatability,and it will be an effective method in correcting distillation ranges and octane number of reformed gasoline.
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.
