In the application of regression analysis method to model dam deformation, the ill-condition problem occurred in coefficient matrix always prevents an accurate modeling mainly due to the multicollinearity of the varia...In the application of regression analysis method to model dam deformation, the ill-condition problem occurred in coefficient matrix always prevents an accurate modeling mainly due to the multicollinearity of the variables. Independent component regression （ICR） was proposed to model the dam deformation and identify the physical origins of the deformation. Simulation experiment shows that ICR can successfully resolve the problem of ill-condition and produce a reliable deformation model. After that, the method is applied to model the deformation of the Wuqiangxi Dam in Hunan province, China. The result shows that ICR can not only accurately model the deformation of the dam, but also help to identify the physical factors that affect the deformation through the extracted independent components.展开更多
Independent component analysis(ICA) can reveal the essential underlying structure of data, and independent component regression(ICR) methods usually obtain better performance than other regression methods such as prin...Independent component analysis(ICA) can reveal the essential underlying structure of data, and independent component regression(ICR) methods usually obtain better performance than other regression methods such as principal component regression. However, when existing ICR methods separate or extract independent components using prewhitened data, the backward propagation of inevitable prewhitened errors deteriorates the final linear prediction accuracy. To overcome this weakness, first, we proposed using weighted orthogonal constraint condition to replace the prewhitening of the data in ICA. Next, the statistical independence of ICs and the close relationship between ICs and quality variables are considered at the same time. Then, by combining the merits of improved ICR and ensemble ICR algorithm which solved the problem of selecting an appropriate nonquadratic function in ICA iteration procedure, a modified independent component regression(MICR) method that directly used the measured process data was proposed. Finally, three experimental results were used to validate excellent performance of modified algorithm.展开更多
Financial time series forecasting could be beneficial for individual as well as institutional investors. But, the high noise and complexity residing in the financial data make this job extremely challenging. Over the ...Financial time series forecasting could be beneficial for individual as well as institutional investors. But, the high noise and complexity residing in the financial data make this job extremely challenging. Over the years, many researchers have used support vector regression (SVR) quite successfully to conquer this challenge. In this paper, an SVR based forecasting model is proposed which first uses the principal component analysis (PCA) to extract the low-dimensional and efficient feature information, and then uses the independent component analysis (ICA) to preprocess the extracted features to nullify the influence of noise in the features. Experiments were carried out based on 16 years’ historical data of three prominent stocks from three different sectors listed in Dhaka Stock Exchange (DSE), Bangladesh. The predictions were made for 1 to 4 days in advance targeting the short term prediction. For comparison, the integration of PCA with SVR (PCA-SVR), ICA with SVR (ICA-SVR) and single SVR approaches were applied to evaluate the prediction accuracy of the proposed approach. Experimental results show that the proposed model (PCA-ICA-SVR) outperforms the PCA-SVR, ICA-SVR and single SVR methods.展开更多
基金Project(41074004)supported by the National Natural Science Foundation of ChinaProject(2013CB733303)supported by the National Basic Research Program of China
文摘In the application of regression analysis method to model dam deformation, the ill-condition problem occurred in coefficient matrix always prevents an accurate modeling mainly due to the multicollinearity of the variables. Independent component regression （ICR） was proposed to model the dam deformation and identify the physical origins of the deformation. Simulation experiment shows that ICR can successfully resolve the problem of ill-condition and produce a reliable deformation model. After that, the method is applied to model the deformation of the Wuqiangxi Dam in Hunan province, China. The result shows that ICR can not only accurately model the deformation of the dam, but also help to identify the physical factors that affect the deformation through the extracted independent components.
基金Sponsored by the National Natural Science Foundation of China(Grant No.61573014)
文摘Independent component analysis(ICA) can reveal the essential underlying structure of data, and independent component regression(ICR) methods usually obtain better performance than other regression methods such as principal component regression. However, when existing ICR methods separate or extract independent components using prewhitened data, the backward propagation of inevitable prewhitened errors deteriorates the final linear prediction accuracy. To overcome this weakness, first, we proposed using weighted orthogonal constraint condition to replace the prewhitening of the data in ICA. Next, the statistical independence of ICs and the close relationship between ICs and quality variables are considered at the same time. Then, by combining the merits of improved ICR and ensemble ICR algorithm which solved the problem of selecting an appropriate nonquadratic function in ICA iteration procedure, a modified independent component regression(MICR) method that directly used the measured process data was proposed. Finally, three experimental results were used to validate excellent performance of modified algorithm.
文摘Financial time series forecasting could be beneficial for individual as well as institutional investors. But, the high noise and complexity residing in the financial data make this job extremely challenging. Over the years, many researchers have used support vector regression (SVR) quite successfully to conquer this challenge. In this paper, an SVR based forecasting model is proposed which first uses the principal component analysis (PCA) to extract the low-dimensional and efficient feature information, and then uses the independent component analysis (ICA) to preprocess the extracted features to nullify the influence of noise in the features. Experiments were carried out based on 16 years’ historical data of three prominent stocks from three different sectors listed in Dhaka Stock Exchange (DSE), Bangladesh. The predictions were made for 1 to 4 days in advance targeting the short term prediction. For comparison, the integration of PCA with SVR (PCA-SVR), ICA with SVR (ICA-SVR) and single SVR approaches were applied to evaluate the prediction accuracy of the proposed approach. Experimental results show that the proposed model (PCA-ICA-SVR) outperforms the PCA-SVR, ICA-SVR and single SVR methods.
