Choosing optimal parameters for support vector regression （SVR） is an important step in SVR. design, which strongly affects the pefformance of SVR. In this paper, based on the analysis of influence of SVR parameters...Choosing optimal parameters for support vector regression （SVR） is an important step in SVR. design, which strongly affects the pefformance of SVR. In this paper, based on the analysis of influence of SVR parameters on generalization error, a new approach with two steps is proposed for selecting SVR parameters, First the kernel function and SVM parameters are optimized roughly through genetic algorithm, then the kernel parameter is finely adjusted by local linear search, This approach has been successfully applied to the prediction model of the sulfur content in hot metal. The experiment results show that the proposed approach can yield better generalization performance of SVR than other methods,展开更多
Metamodeling techniques have been used in robust optimization to reduce the high computational cost of the uncertainty analysis and improve the performance of robust optimization problems with computationally expensiv...Metamodeling techniques have been used in robust optimization to reduce the high computational cost of the uncertainty analysis and improve the performance of robust optimization problems with computationally expensive simulation models. Existing metamodels main focus on polynomial regression（PR）, neural networks（NN） and Kriging models, these metamodels are not well suited for large-scale robust optimization problems with small size training sets and high nonlinearity. To address the problem, a reduced approximation model technique based on support vector regression（SVR） is introduced in order to improve the accuracy of metamodels. A robust optimization method based on SVR is presented for problems that involve high dimension and nonlinear. First appropriate design parameter samples are selected by experimental design theories, then the response samples are obtained from the simulations such as finite element analysis, the SVR metamodel is constructed and treated as the mean and the variance of the objective performance functions. Combining other constraints, the robust optimization model is formed which can be solved by genetic algorithm （GA）. The applicability of the method developed is demonstrated using a case of two-bar structure system study. The performances of SVR were compared with those of PR, Kriging and back-propagation neural networks（BPNN）, the comparison results show that the prediction accuracy of the SVR metamodel was higher than those of other metamodels under uncertainty. The robust optimization solutions are near to the real result, and the proposed method is found to be accurate and efficient for robust optimization. This reaserch provides an efficient method for robust optimization problems with complex structure.展开更多
Due to the severe and costly problems caused by asphaltene precipitation in petroleum industry,developing a quick and accurate model,to predict the asphaltene precipitation under different conditions,seems crucial.In ...Due to the severe and costly problems caused by asphaltene precipitation in petroleum industry,developing a quick and accurate model,to predict the asphaltene precipitation under different conditions,seems crucial.In this study,a new model,namely genetic algorithm e support vector regression(GA-SVR)is proposed,which is applied to predict the amount of asphaltene precipitation.GA is used to select the best optimal values of SVR parameters and kernel parameter,simultaneously,to increase the generalization performance of the SVR.The GA-SVR model is trained and tested on the experimental data sets reported in literature.The performance of the GASVR model is compared with two scaling equation models,using statistical error measures and graphical analyses.The results show that the prediction performance of the proposed model,is highly reliable and satisfactory.展开更多
文摘Choosing optimal parameters for support vector regression （SVR） is an important step in SVR. design, which strongly affects the pefformance of SVR. In this paper, based on the analysis of influence of SVR parameters on generalization error, a new approach with two steps is proposed for selecting SVR parameters, First the kernel function and SVM parameters are optimized roughly through genetic algorithm, then the kernel parameter is finely adjusted by local linear search, This approach has been successfully applied to the prediction model of the sulfur content in hot metal. The experiment results show that the proposed approach can yield better generalization performance of SVR than other methods,
基金supported by National Natural Science Foundation of China (Grant No.60572007)National Basic Research Program of China(973 Program,Grant No.613580202)
文摘Metamodeling techniques have been used in robust optimization to reduce the high computational cost of the uncertainty analysis and improve the performance of robust optimization problems with computationally expensive simulation models. Existing metamodels main focus on polynomial regression（PR）, neural networks（NN） and Kriging models, these metamodels are not well suited for large-scale robust optimization problems with small size training sets and high nonlinearity. To address the problem, a reduced approximation model technique based on support vector regression（SVR） is introduced in order to improve the accuracy of metamodels. A robust optimization method based on SVR is presented for problems that involve high dimension and nonlinear. First appropriate design parameter samples are selected by experimental design theories, then the response samples are obtained from the simulations such as finite element analysis, the SVR metamodel is constructed and treated as the mean and the variance of the objective performance functions. Combining other constraints, the robust optimization model is formed which can be solved by genetic algorithm （GA）. The applicability of the method developed is demonstrated using a case of two-bar structure system study. The performances of SVR were compared with those of PR, Kriging and back-propagation neural networks（BPNN）, the comparison results show that the prediction accuracy of the SVR metamodel was higher than those of other metamodels under uncertainty. The robust optimization solutions are near to the real result, and the proposed method is found to be accurate and efficient for robust optimization. This reaserch provides an efficient method for robust optimization problems with complex structure.
文摘Due to the severe and costly problems caused by asphaltene precipitation in petroleum industry,developing a quick and accurate model,to predict the asphaltene precipitation under different conditions,seems crucial.In this study,a new model,namely genetic algorithm e support vector regression(GA-SVR)is proposed,which is applied to predict the amount of asphaltene precipitation.GA is used to select the best optimal values of SVR parameters and kernel parameter,simultaneously,to increase the generalization performance of the SVR.The GA-SVR model is trained and tested on the experimental data sets reported in literature.The performance of the GASVR model is compared with two scaling equation models,using statistical error measures and graphical analyses.The results show that the prediction performance of the proposed model,is highly reliable and satisfactory.
文摘为提高光伏发电功率的预测精度,针对支持向量机回归(Support Vector Regression,SVR)模型的预测结果易受其惩罚系数C、敏感损失函数的最大误差系数ε和核函数g影响的问题,提出一种基于新型智能算法-蝗虫算法优化SVR模型参数的光伏发电功率预测模型。由于光伏发电功率数据存在随机性和间隙性的特征,Multi-Agent和分布式思想被引入蝗虫算法优化SVR模型,通过将云计算的MapReduce框架和GOA-SVR结合,提出一种基于MapReduce和GOA-SVR并行化的光伏发电功率预测模型(MapReduce and GOA-SVR,MR-GOA-SVR),从而提高海量高维光伏发电数据的处理能力。将影响光伏输出功率的11个气象因素作为GOA-SVR的输入向量,光伏输出功率作为GOA-SVR的输出向量,建立GOA-SVR的光伏发电功率预测模型。研究结果表明:MR-GOA-SVR可以有效提高不同天气类型下的光伏发电功率的预测精度,具有很强的现实性和指导意义。与PSO-SVR、GA-SVR、GOA-SVR和SVR相比,MR-GOA-SVR在晴天、阴天和雨天均可以提高预测精度,且具有优异的并行性能。
