Analysis of stock recruitment (SR) data is most often done by fitting various SR relationship curves to the data. Fish population dynamics data often have stochastic variations and measurement errors, which usually re...Analysis of stock recruitment (SR) data is most often done by fitting various SR relationship curves to the data. Fish population dynamics data often have stochastic variations and measurement errors, which usually result in a biased regression analysis. This paper presents a robust regression method, least median of squared orthogonal distance (LMD), which is insensitive to abnormal values in the dependent and independent variables in a regression analysis. Outliers that have significantly different variance from the rest of the data can be identified in a residual analysis. Then, the least squares (LS) method is applied to the SR data with defined outliers being down weighted. The application of LMD and LMD based Reweighted Least Squares (RLS) method to simulated and real fisheries SR data is explored.展开更多
Through theoretical derivation, some properties of the total least squares estimation are found. The total least squares estimation is the linear transformation of the least squares estimation, and the total least squ...Through theoretical derivation, some properties of the total least squares estimation are found. The total least squares estimation is the linear transformation of the least squares estimation, and the total least squares estimation is unbiased. The condition number of the total least squares estimation is greater than the least squares estimation, so the total least squares estimation is easier to be affected by the data error than the least squares estimation. Then through the further derivation, the relationships of solutions, residuals and unit weight variance estimations between the total least squares and the least squares are given.展开更多
As the solutions of the least squares support vector regression machine (LS-SVRM) are not sparse, it leads to slow prediction speed and limits its applications. The defects of the ex- isting adaptive pruning algorit...As the solutions of the least squares support vector regression machine (LS-SVRM) are not sparse, it leads to slow prediction speed and limits its applications. The defects of the ex- isting adaptive pruning algorithm for LS-SVRM are that the training speed is slow, and the generalization performance is not satis- factory, especially for large scale problems. Hence an improved algorithm is proposed. In order to accelerate the training speed, the pruned data point and fast leave-one-out error are employed to validate the temporary model obtained after decremental learning. The novel objective function in the termination condition which in- volves the whole constraints generated by all training data points and three pruning strategies are employed to improve the generali- zation performance. The effectiveness of the proposed algorithm is tested on six benchmark datasets. The sparse LS-SVRM model has a faster training speed and better generalization performance.展开更多
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
Least squares support vector machines (LS-SVMs), a nonlinear kemel based machine was introduced to investigate the prospects of application of this approach in modelling water vapor and carbon dioxide fluxes above a s...Least squares support vector machines (LS-SVMs), a nonlinear kemel based machine was introduced to investigate the prospects of application of this approach in modelling water vapor and carbon dioxide fluxes above a summer maize field using the dataset obtained in the North China Plain with eddy covariance technique. The performances of the LS-SVMs were compared to the corresponding models obtained with radial basis function (RBF) neural networks. The results indicated the trained LS-SVMs with a radial basis function kernel had satisfactory performance in modelling surface fluxes; its excellent approximation and generalization property shed new light on the study on complex processes in ecosystem.展开更多
针对现有电力电子电路故障预测技术的不足,提出将电路特征性能参数和最小二乘支持向量机(least squares support vector machine,LS-SVM)预测算法结合,对电力电子电路进行故障预测。以Buck电路为例,选择电路输出电压作为监测信号,提取...针对现有电力电子电路故障预测技术的不足,提出将电路特征性能参数和最小二乘支持向量机(least squares support vector machine,LS-SVM)预测算法结合,对电力电子电路进行故障预测。以Buck电路为例,选择电路输出电压作为监测信号,提取输出电压平均值及纹波值作为电路特征性能参数,并利用LS-SVM回归算法实现故障预测。实验结果表明,利用LS-SVM对电路输出平均电压与输出纹波电压的预测相对误差均低于2%,能够跟踪故障特征性能参数的变化趋势,有效实现电力电子电路故障预测。展开更多
提出一种基于最小二乘支持向量机(least square support vector machine,LS-SVM)在线误差补偿非线性动态逆控制器设计方案。首先运用动态逆的双阶段设计方法设计了导弹的逆控制器,即第一阶段采用动态逆方法设计快回路控制器实现对滚转...提出一种基于最小二乘支持向量机(least square support vector machine,LS-SVM)在线误差补偿非线性动态逆控制器设计方案。首先运用动态逆的双阶段设计方法设计了导弹的逆控制器,即第一阶段采用动态逆方法设计快回路控制器实现对滚转、偏航和俯仰三个通道角速度的跟踪;第二阶段实现慢回路对滚转角、侧滑角和攻角的跟踪;然后,设计LS-SVM在线补偿器,以增强导弹控制系统的鲁棒性。通过仿真分析,验证了该方法的有效性。展开更多
基于无线接入点(Access Point,AP)接收信号强度(Received Signal Strength,RSS)的位置指纹室内定位技术近几年已经成为国内外位置感知研究的热点。提出了基于最小二乘支持向量机(Least Squares Support Vector Machines,LS-SVM)的位置...基于无线接入点(Access Point,AP)接收信号强度(Received Signal Strength,RSS)的位置指纹室内定位技术近几年已经成为国内外位置感知研究的热点。提出了基于最小二乘支持向量机(Least Squares Support Vector Machines,LS-SVM)的位置指纹定位方法。给出了基于LS-SVM的指纹定位模型,描述了LS-SVM指纹样本训练的具体实现过程。重点在于将定位问题转化为一个多类别分类问题,并分别采用一对一(OAO)和一对多(OAA)方法将其转化为多个二值分类问题。仿真结果表明,LS-SVM较传统支持向量机(SVMs)、K近邻(k-Nearest Neighbors,K-NN)定位方法的分类准确率高且计算代价小,平均分类准确率达92.00%。展开更多
文摘Analysis of stock recruitment (SR) data is most often done by fitting various SR relationship curves to the data. Fish population dynamics data often have stochastic variations and measurement errors, which usually result in a biased regression analysis. This paper presents a robust regression method, least median of squared orthogonal distance (LMD), which is insensitive to abnormal values in the dependent and independent variables in a regression analysis. Outliers that have significantly different variance from the rest of the data can be identified in a residual analysis. Then, the least squares (LS) method is applied to the SR data with defined outliers being down weighted. The application of LMD and LMD based Reweighted Least Squares (RLS) method to simulated and real fisheries SR data is explored.
基金The research was supported by the National Natural Science Foundation of China(41204003)Scientific Research Foundation of ECIT(DHBK201113)Scientific Research Foundation of Jiangxi Province Key Laboratory for Digital Land(DLLJ201207)
文摘Through theoretical derivation, some properties of the total least squares estimation are found. The total least squares estimation is the linear transformation of the least squares estimation, and the total least squares estimation is unbiased. The condition number of the total least squares estimation is greater than the least squares estimation, so the total least squares estimation is easier to be affected by the data error than the least squares estimation. Then through the further derivation, the relationships of solutions, residuals and unit weight variance estimations between the total least squares and the least squares are given.
基金supported by the National Natural Science Foundation of China (61074127)
文摘As the solutions of the least squares support vector regression machine (LS-SVRM) are not sparse, it leads to slow prediction speed and limits its applications. The defects of the ex- isting adaptive pruning algorithm for LS-SVRM are that the training speed is slow, and the generalization performance is not satis- factory, especially for large scale problems. Hence an improved algorithm is proposed. In order to accelerate the training speed, the pruned data point and fast leave-one-out error are employed to validate the temporary model obtained after decremental learning. The novel objective function in the termination condition which in- volves the whole constraints generated by all training data points and three pruning strategies are employed to improve the generali- zation performance. The effectiveness of the proposed algorithm is tested on six benchmark datasets. The sparse LS-SVRM model has a faster training speed and better generalization performance.
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.
基金Project supported by the National Science Fund for OutstandingYouth Overseas (No. 40328001) and the Key Research Plan of theKnowledge Innovation Project of the Institute of Geographic Sciencesand Natural Resources, Chinese Academy of Sciences (No.KZCXI-SW-01)
文摘Least squares support vector machines (LS-SVMs), a nonlinear kemel based machine was introduced to investigate the prospects of application of this approach in modelling water vapor and carbon dioxide fluxes above a summer maize field using the dataset obtained in the North China Plain with eddy covariance technique. The performances of the LS-SVMs were compared to the corresponding models obtained with radial basis function (RBF) neural networks. The results indicated the trained LS-SVMs with a radial basis function kernel had satisfactory performance in modelling surface fluxes; its excellent approximation and generalization property shed new light on the study on complex processes in ecosystem.
文摘针对现有电力电子电路故障预测技术的不足,提出将电路特征性能参数和最小二乘支持向量机(least squares support vector machine,LS-SVM)预测算法结合,对电力电子电路进行故障预测。以Buck电路为例,选择电路输出电压作为监测信号,提取输出电压平均值及纹波值作为电路特征性能参数,并利用LS-SVM回归算法实现故障预测。实验结果表明,利用LS-SVM对电路输出平均电压与输出纹波电压的预测相对误差均低于2%,能够跟踪故障特征性能参数的变化趋势,有效实现电力电子电路故障预测。
文摘提出一种基于最小二乘支持向量机(least square support vector machine,LS-SVM)在线误差补偿非线性动态逆控制器设计方案。首先运用动态逆的双阶段设计方法设计了导弹的逆控制器,即第一阶段采用动态逆方法设计快回路控制器实现对滚转、偏航和俯仰三个通道角速度的跟踪;第二阶段实现慢回路对滚转角、侧滑角和攻角的跟踪;然后,设计LS-SVM在线补偿器,以增强导弹控制系统的鲁棒性。通过仿真分析,验证了该方法的有效性。
文摘基于无线接入点(Access Point,AP)接收信号强度(Received Signal Strength,RSS)的位置指纹室内定位技术近几年已经成为国内外位置感知研究的热点。提出了基于最小二乘支持向量机(Least Squares Support Vector Machines,LS-SVM)的位置指纹定位方法。给出了基于LS-SVM的指纹定位模型,描述了LS-SVM指纹样本训练的具体实现过程。重点在于将定位问题转化为一个多类别分类问题,并分别采用一对一(OAO)和一对多(OAA)方法将其转化为多个二值分类问题。仿真结果表明,LS-SVM较传统支持向量机(SVMs)、K近邻(k-Nearest Neighbors,K-NN)定位方法的分类准确率高且计算代价小,平均分类准确率达92.00%。