Removal of cloud cover on the satellite remote sensing image can effectively improve the availability of remote sensing images. For thin cloud cover, support vector value contourlet transform is used to achieve multi-...Removal of cloud cover on the satellite remote sensing image can effectively improve the availability of remote sensing images. For thin cloud cover, support vector value contourlet transform is used to achieve multi-scale decomposition of the area of thin cloud cover on remote sensing images. Through enhancing coefficients of high frequency and suppressing coefficients of low frequency, the thin cloud is removed. For thick cloud cover, if the areas of thick cloud cover on multi-source or multi-temporal remote sensing images do not overlap, the multi-output support vector regression learning method is used to remove this kind of thick clouds. If the thick cloud cover areas overlap, by using the multi-output learning of the surrounding areas to predict the surface features of the overlapped thick cloud cover areas, this kind of thick cloud is removed. Experimental results show that the proposed cloud removal method can effectively solve the problems of the cloud overlapping and radiation difference among multi-source images. The cloud removal image is clear and smooth.展开更多
An on-line forecasting model based on self-tuning support vectors regression for zinc output was put forward to maximize zinc output by adjusting operational parameters in the process of imperial smelting furnace. In ...An on-line forecasting model based on self-tuning support vectors regression for zinc output was put forward to maximize zinc output by adjusting operational parameters in the process of imperial smelting furnace. In this model, the mathematical model of support vector regression was converted into the same format as support vector machine for classification. Then a simplified sequential minimal optimization for classification was applied to train the regression coefficient vector α- α* and threshold b. Sequentially penalty parameter C was tuned dynamically through forecasting result during the training process. Finally, an on-line forecasting algorithm for zinc output was proposed. The simulation result shows that in spite of a relatively small industrial data set, the effective error is less than 10% with a remarkable performance of real time. The model was applied to the optimization operation and fault diagnosis system for imperial smelting furnace.展开更多
To overcome the disadvantage that the standard least squares support vector regression(LS-SVR) algorithm is not suitable to multiple-input multiple-output(MIMO) system modelling directly,an improved LS-SVR algorithm w...To overcome the disadvantage that the standard least squares support vector regression(LS-SVR) algorithm is not suitable to multiple-input multiple-output(MIMO) system modelling directly,an improved LS-SVR algorithm which was defined as multi-output least squares support vector regression(MLSSVR) was put forward by adding samples' absolute errors in objective function and applied to flatness intelligent control.To solve the poor-precision problem of the control scheme based on effective matrix in flatness control,the predictive control was introduced into the control system and the effective matrix-predictive flatness control method was proposed by combining the merits of the two methods.Simulation experiment was conducted on 900HC reversible cold roll.The performance of effective matrix method and the effective matrix-predictive control method were compared,and the results demonstrate the validity of the effective matrix-predictive control method.展开更多
Considering the modeling errors of on-board self-tuning model in the fault diagnosis of aero-engine, a new mechanism for compensating the model outputs is proposed. A discrete series predictor based on multi-outputs l...Considering the modeling errors of on-board self-tuning model in the fault diagnosis of aero-engine, a new mechanism for compensating the model outputs is proposed. A discrete series predictor based on multi-outputs least square support vector regression （LSSVR） is applied to the compensation of on-board self-tuning model of aero-engine, and particle swarm optimization （PSO） is used to the kernels selection of multi-outputs LSSVR. The method need not reconstruct the model of aero-engine because of the differences in the individuals of the same type engines and engine degradation after use. The concrete steps for the application of the method are given, and the simulation results show the effectiveness of the algorithm.展开更多
As optimization of parameters affects prediction accuracy and generalization ability of support vector regression(SVR) greatly and the predictive model often mismatches nonlinear system model predictive control,a mult...As optimization of parameters affects prediction accuracy and generalization ability of support vector regression(SVR) greatly and the predictive model often mismatches nonlinear system model predictive control,a multi-step model predictive control based on online SVR(OSVR) optimized by multi-agent particle swarm optimization algorithm(MAPSO) is put forward. By integrating the online learning ability of OSVR, the predictive model can self-correct and adapt to the dynamic changes in nonlinear process well.展开更多
A method that applies clustering technique to reduce the number of samples of large data sets using input-output clustering is proposed.The proposed method clusters the output data into groups and clusters the input d...A method that applies clustering technique to reduce the number of samples of large data sets using input-output clustering is proposed.The proposed method clusters the output data into groups and clusters the input data in accordance with the groups of output data.Then,a set of prototypes are selected from the clustered input data.The inessential data can be ultimately discarded from the data set.The proposed method can reduce the effect from outliers because only the prototypes are used.This method is applied to reduce the data set in regression problems.Two standard synthetic data sets and three standard real-world data sets are used for evaluation.The root-mean-square errors are compared from support vector regression models trained with the original data sets and the corresponding instance-reduced data sets.From the experiments,the proposed method provides good results on the reduction and the reconstruction of the standard synthetic and real-world data sets.The numbers of instances of the synthetic data sets are decreased by 25%-69%.The reduction rates for the real-world data sets of the automobile miles per gallon and the 1990 census in CA are 46% and 57%,respectively.The reduction rate of 96% is very good for the electrocardiogram(ECG) data set because of the redundant and periodic nature of ECG signals.For all of the data sets,the regression results are similar to those from the corresponding original data sets.Therefore,the regression performance of the proposed method is good while only a fraction of the data is needed in the training process.展开更多
Based on the ε - support vector regression, three modelling methods for the ship manoeuvring motion, i.e., the white-box modelling, the grey-box modelling and the black-box modelling, are investigated. The 10°/1...Based on the ε - support vector regression, three modelling methods for the ship manoeuvring motion, i.e., the white-box modelling, the grey-box modelling and the black-box modelling, are investigated. The 10°/10°, 20°/20° zigzag tests and the 35° turning circle manoeuvre are simulated. Part of the simulation data for the 20°/20° zigzag test are used to train the support vectors, and the trained support vector machine is used to predict the whole 20° / 20° zigzag test. Comparison between the simula- ted and predicted 20° / 20° zigzag test shows a good predictive ability of the three modelling methods. Then all mathematical models obtained by the modelling methods are used to predict the 10°/10° zigzag test and 35° turning circle manoeuvre, and the predicted results are compared with those of simulation tests to demonstrate the good generalization performance of the mathematical models. Finally, the modelling methods are analyzed and compared with each other in terms of the application conditions, the prediction accuracy and the computation speed. An appropriate modelling method can be chosen according to the intended use of the mathematical models and the available data for the system identification.展开更多
针对空调为二次泵变流量系统时,考虑分区域供冷工况下,采用多目标回归方式解决负荷预测问题将有利于提高负荷预测准确性的情况,提出了两种多目标回归的中央空调负荷预测模型,即多目标支持向量回归(support vector regression,SVR)负荷...针对空调为二次泵变流量系统时,考虑分区域供冷工况下,采用多目标回归方式解决负荷预测问题将有利于提高负荷预测准确性的情况,提出了两种多目标回归的中央空调负荷预测模型,即多目标支持向量回归(support vector regression,SVR)负荷预测模型和多目标长短期记忆(long short term memory,LSTM)神经网络负荷预测模型,利用上海市某医院的二次泵变流量系统数据对两个模型进行训练和预测,并与单目标回归预测模型进行比较.研究结果表明:相较单目标回归预测模型,两种多目标预测模型的预测精度更高;多目标SVR负荷预测模型较多目标LSTM负荷预测模型的预测准确性更高.展开更多
基金supported by the National Natural Science Foundation of China(61172127)the Natural Science Foundation of Anhui Province(1408085MF121)
文摘Removal of cloud cover on the satellite remote sensing image can effectively improve the availability of remote sensing images. For thin cloud cover, support vector value contourlet transform is used to achieve multi-scale decomposition of the area of thin cloud cover on remote sensing images. Through enhancing coefficients of high frequency and suppressing coefficients of low frequency, the thin cloud is removed. For thick cloud cover, if the areas of thick cloud cover on multi-source or multi-temporal remote sensing images do not overlap, the multi-output support vector regression learning method is used to remove this kind of thick clouds. If the thick cloud cover areas overlap, by using the multi-output learning of the surrounding areas to predict the surface features of the overlapped thick cloud cover areas, this kind of thick cloud is removed. Experimental results show that the proposed cloud removal method can effectively solve the problems of the cloud overlapping and radiation difference among multi-source images. The cloud removal image is clear and smooth.
文摘An on-line forecasting model based on self-tuning support vectors regression for zinc output was put forward to maximize zinc output by adjusting operational parameters in the process of imperial smelting furnace. In this model, the mathematical model of support vector regression was converted into the same format as support vector machine for classification. Then a simplified sequential minimal optimization for classification was applied to train the regression coefficient vector α- α* and threshold b. Sequentially penalty parameter C was tuned dynamically through forecasting result during the training process. Finally, an on-line forecasting algorithm for zinc output was proposed. The simulation result shows that in spite of a relatively small industrial data set, the effective error is less than 10% with a remarkable performance of real time. The model was applied to the optimization operation and fault diagnosis system for imperial smelting furnace.
基金Project(50675186) supported by the National Natural Science Foundation of China
文摘To overcome the disadvantage that the standard least squares support vector regression(LS-SVR) algorithm is not suitable to multiple-input multiple-output(MIMO) system modelling directly,an improved LS-SVR algorithm which was defined as multi-output least squares support vector regression(MLSSVR) was put forward by adding samples' absolute errors in objective function and applied to flatness intelligent control.To solve the poor-precision problem of the control scheme based on effective matrix in flatness control,the predictive control was introduced into the control system and the effective matrix-predictive flatness control method was proposed by combining the merits of the two methods.Simulation experiment was conducted on 900HC reversible cold roll.The performance of effective matrix method and the effective matrix-predictive control method were compared,and the results demonstrate the validity of the effective matrix-predictive control method.
文摘Considering the modeling errors of on-board self-tuning model in the fault diagnosis of aero-engine, a new mechanism for compensating the model outputs is proposed. A discrete series predictor based on multi-outputs least square support vector regression （LSSVR） is applied to the compensation of on-board self-tuning model of aero-engine, and particle swarm optimization （PSO） is used to the kernels selection of multi-outputs LSSVR. The method need not reconstruct the model of aero-engine because of the differences in the individuals of the same type engines and engine degradation after use. The concrete steps for the application of the method are given, and the simulation results show the effectiveness of the algorithm.
基金the National Natural Science Foundation of China(No.60905066)the Natural Science Foundation of Chongqing(No.cstc2018jcyjA0667)
文摘As optimization of parameters affects prediction accuracy and generalization ability of support vector regression(SVR) greatly and the predictive model often mismatches nonlinear system model predictive control,a multi-step model predictive control based on online SVR(OSVR) optimized by multi-agent particle swarm optimization algorithm(MAPSO) is put forward. By integrating the online learning ability of OSVR, the predictive model can self-correct and adapt to the dynamic changes in nonlinear process well.
基金supported by Chiang Mai University Research Fund under the contract number T-M5744
文摘A method that applies clustering technique to reduce the number of samples of large data sets using input-output clustering is proposed.The proposed method clusters the output data into groups and clusters the input data in accordance with the groups of output data.Then,a set of prototypes are selected from the clustered input data.The inessential data can be ultimately discarded from the data set.The proposed method can reduce the effect from outliers because only the prototypes are used.This method is applied to reduce the data set in regression problems.Two standard synthetic data sets and three standard real-world data sets are used for evaluation.The root-mean-square errors are compared from support vector regression models trained with the original data sets and the corresponding instance-reduced data sets.From the experiments,the proposed method provides good results on the reduction and the reconstruction of the standard synthetic and real-world data sets.The numbers of instances of the synthetic data sets are decreased by 25%-69%.The reduction rates for the real-world data sets of the automobile miles per gallon and the 1990 census in CA are 46% and 57%,respectively.The reduction rate of 96% is very good for the electrocardiogram(ECG) data set because of the redundant and periodic nature of ECG signals.For all of the data sets,the regression results are similar to those from the corresponding original data sets.Therefore,the regression performance of the proposed method is good while only a fraction of the data is needed in the training process.
基金Project supported by the National Natural Science Foundation of China(Grant No.51279106)the Special Research Fund for the Doctoral Program of Higher Education of China(Grant No.20110073110009)
文摘Based on the ε - support vector regression, three modelling methods for the ship manoeuvring motion, i.e., the white-box modelling, the grey-box modelling and the black-box modelling, are investigated. The 10°/10°, 20°/20° zigzag tests and the 35° turning circle manoeuvre are simulated. Part of the simulation data for the 20°/20° zigzag test are used to train the support vectors, and the trained support vector machine is used to predict the whole 20° / 20° zigzag test. Comparison between the simula- ted and predicted 20° / 20° zigzag test shows a good predictive ability of the three modelling methods. Then all mathematical models obtained by the modelling methods are used to predict the 10°/10° zigzag test and 35° turning circle manoeuvre, and the predicted results are compared with those of simulation tests to demonstrate the good generalization performance of the mathematical models. Finally, the modelling methods are analyzed and compared with each other in terms of the application conditions, the prediction accuracy and the computation speed. An appropriate modelling method can be chosen according to the intended use of the mathematical models and the available data for the system identification.
文摘针对空调为二次泵变流量系统时,考虑分区域供冷工况下,采用多目标回归方式解决负荷预测问题将有利于提高负荷预测准确性的情况,提出了两种多目标回归的中央空调负荷预测模型,即多目标支持向量回归(support vector regression,SVR)负荷预测模型和多目标长短期记忆(long short term memory,LSTM)神经网络负荷预测模型,利用上海市某医院的二次泵变流量系统数据对两个模型进行训练和预测,并与单目标回归预测模型进行比较.研究结果表明:相较单目标回归预测模型,两种多目标预测模型的预测精度更高;多目标SVR负荷预测模型较多目标LSTM负荷预测模型的预测准确性更高.
