In order to improve the reliability in torque calculation of SRM,an accurate nonlinear torque model regresses by recursive robust least squares support vector regression(RR-LSSVR)is proposed in this paper.The model is...In order to improve the reliability in torque calculation of SRM,an accurate nonlinear torque model regresses by recursive robust least squares support vector regression(RR-LSSVR)is proposed in this paper.The model is in terms of a segmented-rotor switched reluctance motor(SSRM).The characteristics of the SSRM is introduced to show its nonlinear characteristics both in magnetic and torque.Then,its mathematic model is established,and an accurate inductance measurement method and a torque calculation method are presented.After this,the principle of the RR-LSSVR and why it can adjust weights according to errors are described.The model used the RR-LSSVR algorithm shows an outstanding capability in accuracy and quickness compared with other algorithms.Finally,to further validate the accuracy of the proposed model in practical application,simulation and experiment are designed based on a 16/10 SSRM.展开更多
Landslide probability prediction plays an important role in understanding landslide information in advance and taking preventive measures.Many factors can influence the occurrence of landslides,which is easy to have a...Landslide probability prediction plays an important role in understanding landslide information in advance and taking preventive measures.Many factors can influence the occurrence of landslides,which is easy to have a curse of dimensionality and thus lead to reduce prediction accuracy.Then the generalization ability of the model will also decline sharply when there are only small samples.To reduce the dimension of calculation and balance the model’s generalization and learning ability,this study proposed a landslide prediction method based on improved principal component analysis(PCA)and mixed kernel function least squares support vector regression(LSSVR)model.First,the traditional PCA was introduced with the idea of linear discrimination,and the dimensions of initial influencing factors were reduced from 8 to 3.The improved PCA can not only weight variables but also extract the original feature.Furthermore,combined with global and local kernel function,the mixed kernel function LSSVR model was framed to improve the generalization ability.Whale optimization algorithm(WOA)was used to optimize the parameters.Moreover,Root Mean Square Error(RMSE),the sum of squared errors(SSE),Mean Absolute Error(MAE),Mean Absolute Precentage Error(MAPE),and reliability were employed to verify the performance of the model.Compared with radial basis function(RBF)LSSVR model,Elman neural network model,and fuzzy decision model,the proposed method has a smaller deviation.Finally,the landslide warning level obtained from the landslide probability can also provide references for relevant decision-making departments in emergency response.展开更多
基金This work was supported by the National Natural Science Foundation of China under Project 51875261the Natural Science Foundation of Jiangsu Province of China under Projects BK20180046 and BK20170071the“Qinglan project”of Jiangsu Province,and the Key Project of Natural Science Foundation of Jiangsu Higher Education Institutions under Project 17KJA460005.
文摘In order to improve the reliability in torque calculation of SRM,an accurate nonlinear torque model regresses by recursive robust least squares support vector regression(RR-LSSVR)is proposed in this paper.The model is in terms of a segmented-rotor switched reluctance motor(SSRM).The characteristics of the SSRM is introduced to show its nonlinear characteristics both in magnetic and torque.Then,its mathematic model is established,and an accurate inductance measurement method and a torque calculation method are presented.After this,the principle of the RR-LSSVR and why it can adjust weights according to errors are described.The model used the RR-LSSVR algorithm shows an outstanding capability in accuracy and quickness compared with other algorithms.Finally,to further validate the accuracy of the proposed model in practical application,simulation and experiment are designed based on a 16/10 SSRM.
基金supported by the Natural Science Foundation of Shaanxi Province(Grant No.2019JQ206)in part by the Science and Technology Department of Shaanxi Province(Grant No.2020CGXNG-009)in part by the Education Department of Shaanxi Province under Grant 17JK0346.
文摘Landslide probability prediction plays an important role in understanding landslide information in advance and taking preventive measures.Many factors can influence the occurrence of landslides,which is easy to have a curse of dimensionality and thus lead to reduce prediction accuracy.Then the generalization ability of the model will also decline sharply when there are only small samples.To reduce the dimension of calculation and balance the model’s generalization and learning ability,this study proposed a landslide prediction method based on improved principal component analysis(PCA)and mixed kernel function least squares support vector regression(LSSVR)model.First,the traditional PCA was introduced with the idea of linear discrimination,and the dimensions of initial influencing factors were reduced from 8 to 3.The improved PCA can not only weight variables but also extract the original feature.Furthermore,combined with global and local kernel function,the mixed kernel function LSSVR model was framed to improve the generalization ability.Whale optimization algorithm(WOA)was used to optimize the parameters.Moreover,Root Mean Square Error(RMSE),the sum of squared errors(SSE),Mean Absolute Error(MAE),Mean Absolute Precentage Error(MAPE),and reliability were employed to verify the performance of the model.Compared with radial basis function(RBF)LSSVR model,Elman neural network model,and fuzzy decision model,the proposed method has a smaller deviation.Finally,the landslide warning level obtained from the landslide probability can also provide references for relevant decision-making departments in emergency response.
