The sea surface temperature （SST） has substantial impacts on the climate; however, due to its highly nonlinear nature, evidently non-periodic and strongly stochastic properties, it is rather difficult to predict SST...The sea surface temperature （SST） has substantial impacts on the climate; however, due to its highly nonlinear nature, evidently non-periodic and strongly stochastic properties, it is rather difficult to predict SST. Here, the authors combine the complementary ensemble empirical mode decomposition （CEEMD） and support vector machine （SVM） methods to predict SST. Extensive tests from several different aspects are presented to validate the effectiveness of the CEEMD-SVM method. The results suggest that the new method works well in forecasting Northeast Pacific SST at a 12-month lead time, with an average absolute error of approximately 0.3℃ and a correlation coefficient of 0.85. Moreover, no spring predictability barrier is observed in our experiments.展开更多
电动汽车电驱动系统高频加速噪声严重影响整车声品质。为此,通过电驱动系统振动噪声试验,采集多工况加速噪声信号,并进行主、客观评价。结合相关性分析以心理声学参数为输入,通过改进的灰狼算法(improved gray wolf optimizer,IGWO)优...电动汽车电驱动系统高频加速噪声严重影响整车声品质。为此,通过电驱动系统振动噪声试验,采集多工况加速噪声信号,并进行主、客观评价。结合相关性分析以心理声学参数为输入,通过改进的灰狼算法(improved gray wolf optimizer,IGWO)优化支持向量回归(support vector regression,SVR),建立IGWO-SVR模型用于电驱动系统声品质预测。引入互补集合经验模态分解(complementary ensemble empirical mode decomposition,CEEMD)与信号的均方根值(root mean square,RMS),提取电驱动系统加速噪声的CEEMD-RMS特征,并建立以CEEMD-RMS为输入的IGWO-SVR声品质预测模型。检验结果表明:以CEEMD-RMS特征为输入的声品质预测模型,预测效果较以心理声学参数为输入的IGWO-SVR模型更优,测试集平均相对误差由8.88%减小为4.18%。展开更多
基金supported in part by the Major Research Plan of the National Natural Science Foundation of China[grant number91530204]the State Key Program of the National Natural Science Foundation of China[grant number 41430426]
文摘The sea surface temperature （SST） has substantial impacts on the climate; however, due to its highly nonlinear nature, evidently non-periodic and strongly stochastic properties, it is rather difficult to predict SST. Here, the authors combine the complementary ensemble empirical mode decomposition （CEEMD） and support vector machine （SVM） methods to predict SST. Extensive tests from several different aspects are presented to validate the effectiveness of the CEEMD-SVM method. The results suggest that the new method works well in forecasting Northeast Pacific SST at a 12-month lead time, with an average absolute error of approximately 0.3℃ and a correlation coefficient of 0.85. Moreover, no spring predictability barrier is observed in our experiments.
