A method of multiple outputs least squares support vector regression （LS-SVR） was developed and described in detail, with the radial basis function （RBF） as the kernel function. The method was applied to predict t...A method of multiple outputs least squares support vector regression （LS-SVR） was developed and described in detail, with the radial basis function （RBF） as the kernel function. The method was applied to predict the future state of the power-shift steering transmission （PSST）. A prediction model of PSST was gotten with multiple outputs LS-SVR. The model performance was greatly influenced by the penalty parameter γ and kernel parameter σ2 which were optimized using cross validation method. The training and prediction of the model were done with spectrometric oil analysis data. The predictive and actual values were compared and a fault in the second PSST was found. The research proved that this method had good accuracy in PSST fault prediction, and any possible problem in PSST could be found through a comparative analysis.展开更多
针对目前常用的基于参数化非线性模型(Parameterized Nonlinear Model,PNM)的补偿算法存在易陷入局部最小值,导致补偿性能不稳的问题,该文提出了基于最小二乘支持向量机(Least Squares Support Vector Machine,LS-SVM)的宽带接收前端非...针对目前常用的基于参数化非线性模型(Parameterized Nonlinear Model,PNM)的补偿算法存在易陷入局部最小值,导致补偿性能不稳的问题,该文提出了基于最小二乘支持向量机(Least Squares Support Vector Machine,LS-SVM)的宽带接收前端非线性补偿算法.该算法基于减谱-时频变换法(Spectrum Reduction Algorithm based on Time-Frequency Conversion,SRA-TFC)盲分离接收前端输出信号中的大功率基波信号和其他小功率信号,并以此作为LS-SVM逆模型的训练输入-输出样本对.引入最小二乘支持向量回归(Least Squares Support Vector Regression,LS-SVR)算法高精度拟合接收前端非线性逆模型.通过以宽带接收前端的输出信号为测试样本消除其非线性失真分量.仿真与实测结果表明:该算法可使宽带接收前端的无杂散失真动态范围(Spurs-Free-Dynamic-Range,SFDR)提高约20 dB,较基于PNM的补偿算法提高了约5 dB.展开更多
In this study, potential of Least Square-Support Vector Regression (LS-SVR) approach is utilized to model the daily variation of river flow. Inherent complexity, unavailability of reasonably long data set and heteroge...In this study, potential of Least Square-Support Vector Regression (LS-SVR) approach is utilized to model the daily variation of river flow. Inherent complexity, unavailability of reasonably long data set and heterogeneous catchment response are the couple of issues that hinder the generalization of relationship between previous and forthcoming river flow magnitudes. The problem complexity may get enhanced with the influence of upstream dam releases. These issues are investigated by exploiting the capability of LS-SVR–an approach that considers Structural Risk Minimization (SRM) against the Empirical Risk Minimization (ERM)–used by other learning approaches, such as, Artificial Neural Network (ANN). This study is conducted in upper Narmada river basin in India having Bargi dam in its catchment, constructed in 1989. The river gauging station–Sandia is located few hundred kilometer downstream of Bargi dam. The model development is carried out with pre-construction flow regime and its performance is checked for both pre- and post-construction of the dam for any perceivable difference. It is found that the performances are similar for both the flow regimes, which indicates that the releases from the dam at daily scale for this gauging site may be ignored. In order to investigate the temporal horizon over which the prediction performance may be relied upon, a multistep-ahead prediction is carried out and the model performance is found to be reasonably good up to 5-day-ahead predictions though the performance is decreasing with the increase in lead-time. Skills of both LS-SVR and ANN are reported and it is found that the former performs better than the latter for all the lead-times in general, and shorter lead times in particular.展开更多
Recently, fault or health condition prediction of complex systems becomes an interesting research topic. However, it is difficult to establish precise physical model for complex systems, and the time series properties...Recently, fault or health condition prediction of complex systems becomes an interesting research topic. However, it is difficult to establish precise physical model for complex systems, and the time series properties are often necessary to be incorporated for the prediction in practice. Currently, the LS-SVR is widely adopted for prediction of systems with time series data. In this paper, in order to improve the prediction accuracy, accumulated generating operation （AGO） is carried out to improve the data quality and regularity of raw time series data based on grey system theory; then, the inverse accumulated generating operation （IAGO） is performed to obtain the prediction results. In addition, due to the reason that appropriate kernel function plays an important role in improving the accuracy of prediction through LS-SVR, a modified Gaussian radial basis function （RBF） is proposed. The requirements of distance functions-based kernel functions are satisfied, which ensure fast damping at the place adjacent to the test point and a moderate damping at infinity. The presented model is applied to the analysis of benchmarks. As indicated by the results, the proposed method is an effective prediction one with good precision.展开更多
通过对最小二乘支持向量机(Least squares support vector regression,LS-SVR)滤波特性的分析,给出了LS-SVR用于图像滤波的卷积模板构造方法,解决了LS-SVR在应用中需要求解的问题,在此基础上,提出了基于LS-SVR的开关型椒盐噪声滤波算法...通过对最小二乘支持向量机(Least squares support vector regression,LS-SVR)滤波特性的分析,给出了LS-SVR用于图像滤波的卷积模板构造方法,解决了LS-SVR在应用中需要求解的问题,在此基础上,提出了基于LS-SVR的开关型椒盐噪声滤波算法.滤波算法中以Maximum-minimum算子作为椒盐噪声检测器,利用滤波窗口内非噪声点构成LS-SVR的输入数据,使用事先构造出的LS-SVR滤波算子,对滤波窗口进行简单的卷积运算,实现了被椒盐噪声污染点数据的有效恢复,实验表明,本文提出的方法具有较好的细节保护能力和较强的噪声去除能力.展开更多
最小二乘支持向量回归(the least squares support vector regression,LS-SVR)算法因其回归拟合度高广泛应用于各领域中.以目标物在不同光源下采集的图像呈现出不同的颜色值,从而导致图像与目标物出现视觉上的偏差为研究对象,并以潘通...最小二乘支持向量回归(the least squares support vector regression,LS-SVR)算法因其回归拟合度高广泛应用于各领域中.以目标物在不同光源下采集的图像呈现出不同的颜色值,从而导致图像与目标物出现视觉上的偏差为研究对象,并以潘通色卡为参照,利用LSSVR算法,结合将RGB颜色空间到sRGB颜色空间的转换模型,对测试图像进行矫正处理.实验结果表明:与多项式回归相比,LS-SVR算法能取得更小的色差,且矫正后的图像更接近于目标图像.展开更多
Health trend prediction has become an effective way to ensure the safe operation of highly reliable systems,and online prediction is always necessary in many real applications.To simultaneously obtain better or accept...Health trend prediction has become an effective way to ensure the safe operation of highly reliable systems,and online prediction is always necessary in many real applications.To simultaneously obtain better or acceptable online prediction accuracy and shorter computing time,we propose a new adaptive online method based on least squares support vector regression(LS-SVR).This method adopts two approaches.One approach is that we delete certain support vectors by judging the linear correlation among the samples to increase the sparseness of the prediction model.This approach can control the loss of useful information in sample data,improve the generalization capability of the prediction model,and reduce the prediction time.The other approach is that we reduce the number of traditional LS-SVR parameters and establish a modified simple prediction model.This approach can reduce the calculation time in the process of adaptive online training.Simulation and a certain electric system application indicate preliminarily that the proposed method is an effective prediction approach for its good prediction accuracy and low computing time.展开更多
基金Supported by the Ministerial Level Advanced Research Foundation(3031030)the"111"Project(B08043)
文摘A method of multiple outputs least squares support vector regression （LS-SVR） was developed and described in detail, with the radial basis function （RBF） as the kernel function. The method was applied to predict the future state of the power-shift steering transmission （PSST）. A prediction model of PSST was gotten with multiple outputs LS-SVR. The model performance was greatly influenced by the penalty parameter γ and kernel parameter σ2 which were optimized using cross validation method. The training and prediction of the model were done with spectrometric oil analysis data. The predictive and actual values were compared and a fault in the second PSST was found. The research proved that this method had good accuracy in PSST fault prediction, and any possible problem in PSST could be found through a comparative analysis.
文摘针对目前常用的基于参数化非线性模型(Parameterized Nonlinear Model,PNM)的补偿算法存在易陷入局部最小值,导致补偿性能不稳的问题,该文提出了基于最小二乘支持向量机(Least Squares Support Vector Machine,LS-SVM)的宽带接收前端非线性补偿算法.该算法基于减谱-时频变换法(Spectrum Reduction Algorithm based on Time-Frequency Conversion,SRA-TFC)盲分离接收前端输出信号中的大功率基波信号和其他小功率信号,并以此作为LS-SVM逆模型的训练输入-输出样本对.引入最小二乘支持向量回归(Least Squares Support Vector Regression,LS-SVR)算法高精度拟合接收前端非线性逆模型.通过以宽带接收前端的输出信号为测试样本消除其非线性失真分量.仿真与实测结果表明:该算法可使宽带接收前端的无杂散失真动态范围(Spurs-Free-Dynamic-Range,SFDR)提高约20 dB,较基于PNM的补偿算法提高了约5 dB.
文摘In this study, potential of Least Square-Support Vector Regression (LS-SVR) approach is utilized to model the daily variation of river flow. Inherent complexity, unavailability of reasonably long data set and heterogeneous catchment response are the couple of issues that hinder the generalization of relationship between previous and forthcoming river flow magnitudes. The problem complexity may get enhanced with the influence of upstream dam releases. These issues are investigated by exploiting the capability of LS-SVR–an approach that considers Structural Risk Minimization (SRM) against the Empirical Risk Minimization (ERM)–used by other learning approaches, such as, Artificial Neural Network (ANN). This study is conducted in upper Narmada river basin in India having Bargi dam in its catchment, constructed in 1989. The river gauging station–Sandia is located few hundred kilometer downstream of Bargi dam. The model development is carried out with pre-construction flow regime and its performance is checked for both pre- and post-construction of the dam for any perceivable difference. It is found that the performances are similar for both the flow regimes, which indicates that the releases from the dam at daily scale for this gauging site may be ignored. In order to investigate the temporal horizon over which the prediction performance may be relied upon, a multistep-ahead prediction is carried out and the model performance is found to be reasonably good up to 5-day-ahead predictions though the performance is decreasing with the increase in lead-time. Skills of both LS-SVR and ANN are reported and it is found that the former performs better than the latter for all the lead-times in general, and shorter lead times in particular.
基金supported by National Natural Science Foundation(NNSF)of China under Grant No.61371024Aviation Science Fund of China under Grant No.2013ZD53051+1 种基金Aerospace Technology Support Fund of Chinathe Industry-Academy-Research Project of AVIC(cxy2013XGD14)
文摘Recently, fault or health condition prediction of complex systems becomes an interesting research topic. However, it is difficult to establish precise physical model for complex systems, and the time series properties are often necessary to be incorporated for the prediction in practice. Currently, the LS-SVR is widely adopted for prediction of systems with time series data. In this paper, in order to improve the prediction accuracy, accumulated generating operation （AGO） is carried out to improve the data quality and regularity of raw time series data based on grey system theory; then, the inverse accumulated generating operation （IAGO） is performed to obtain the prediction results. In addition, due to the reason that appropriate kernel function plays an important role in improving the accuracy of prediction through LS-SVR, a modified Gaussian radial basis function （RBF） is proposed. The requirements of distance functions-based kernel functions are satisfied, which ensure fast damping at the place adjacent to the test point and a moderate damping at infinity. The presented model is applied to the analysis of benchmarks. As indicated by the results, the proposed method is an effective prediction one with good precision.
文摘通过对最小二乘支持向量机(Least squares support vector regression,LS-SVR)滤波特性的分析,给出了LS-SVR用于图像滤波的卷积模板构造方法,解决了LS-SVR在应用中需要求解的问题,在此基础上,提出了基于LS-SVR的开关型椒盐噪声滤波算法.滤波算法中以Maximum-minimum算子作为椒盐噪声检测器,利用滤波窗口内非噪声点构成LS-SVR的输入数据,使用事先构造出的LS-SVR滤波算子,对滤波窗口进行简单的卷积运算,实现了被椒盐噪声污染点数据的有效恢复,实验表明,本文提出的方法具有较好的细节保护能力和较强的噪声去除能力.
文摘最小二乘支持向量回归(the least squares support vector regression,LS-SVR)算法因其回归拟合度高广泛应用于各领域中.以目标物在不同光源下采集的图像呈现出不同的颜色值,从而导致图像与目标物出现视觉上的偏差为研究对象,并以潘通色卡为参照,利用LSSVR算法,结合将RGB颜色空间到sRGB颜色空间的转换模型,对测试图像进行矫正处理.实验结果表明:与多项式回归相比,LS-SVR算法能取得更小的色差,且矫正后的图像更接近于目标图像.
基金Project supported by the National Basic Research Program (973) of Chinathe National Natural Science Foundation of China (Nos.61001023 and 61101004)+1 种基金the Basic Research Program of Shaanxi Province,China (No. 2010JQ8005)the Aviation Science Fund of China (No. 2010ZD53039)
文摘Health trend prediction has become an effective way to ensure the safe operation of highly reliable systems,and online prediction is always necessary in many real applications.To simultaneously obtain better or acceptable online prediction accuracy and shorter computing time,we propose a new adaptive online method based on least squares support vector regression(LS-SVR).This method adopts two approaches.One approach is that we delete certain support vectors by judging the linear correlation among the samples to increase the sparseness of the prediction model.This approach can control the loss of useful information in sample data,improve the generalization capability of the prediction model,and reduce the prediction time.The other approach is that we reduce the number of traditional LS-SVR parameters and establish a modified simple prediction model.This approach can reduce the calculation time in the process of adaptive online training.Simulation and a certain electric system application indicate preliminarily that the proposed method is an effective prediction approach for its good prediction accuracy and low computing time.
