The hardness prediction model was established by support vector regression(SVR).In order to avoid exaggerating the contribution of very tiny alloying elements,a weighted fuzzy C-means(WFCM)algorithm was proposed for d...The hardness prediction model was established by support vector regression(SVR).In order to avoid exaggerating the contribution of very tiny alloying elements,a weighted fuzzy C-means(WFCM)algorithm was proposed for data clustering using improved Mahalanobis distance based on random forest importance values,which could play a full role of important features and avoid clustering center overlap.The samples were divided into two classes.The top 10 features of each class were selected to form two feature subsets for better performance of the model.The dimension and dispersion of features decreased in such feature subsets.Comparing four machine learning algorithms,SVR had the best performance and was chosen to modeling.The hyper-parameters of the SVR model were optimized by particle swarm optimization.The samples in validation set were classified according to minimum distance of sample to clustering centers,and then the SVR model trained by feature subset of corresponding class was used for prediction.Compared with the feature subset of original data set,the predicted values of model trained by feature subsets of classified samples by WFCM had higher correlation coefficient and lower root mean square error.It indicated that WFCM was an effective method to reduce the dispersion of features and improve the accuracy of model.展开更多
随着电网公司代理购电业务稳步推进,代理购电业务体系逐步完善,精确的代理购电用户用电量预测为保障电力安全稳定供应奠定了基础。因此,文章构建自适应权重组合模型,将不同校核方法的校核结果进行权重分配,从而提升校核结果准确性。首先...随着电网公司代理购电业务稳步推进,代理购电业务体系逐步完善,精确的代理购电用户用电量预测为保障电力安全稳定供应奠定了基础。因此,文章构建自适应权重组合模型,将不同校核方法的校核结果进行权重分配,从而提升校核结果准确性。首先,构建预测业务偏差校核流程框架,确定代理购电预测业务校核流程。然后分别选取分位数映射法、增量变化法以及支持向量回归(support vector regression,SVR)对预测结果进行校核,得到同一纬度下的不同方法校核结果。最后,建立遗传算法-优劣解距离法(genetic algorithm-technique for order preference by similarity to ideal solution,GA-TOPSIS)模型针对校核结果进行准确性与稳定性双目标优化,选取不同校核方法的最优权重组合。测试结果表明在校核方法权重组合校正后,相较于初始预测值和单一校核方法校核后的结果,预测精度和准确度得到明显提升。展开更多
除了信噪比、有效子波畸变等,稳健性(Robustness)也是度量滤波方法效果的一个重要的物理量,它刻画了滤波系统应对异常点值的能力.一般用影响函数作为评价稳健性的工具.支持向量机方法已较成功地应用于信号与图像的滤波中,尤其Ricker子...除了信噪比、有效子波畸变等,稳健性(Robustness)也是度量滤波方法效果的一个重要的物理量,它刻画了滤波系统应对异常点值的能力.一般用影响函数作为评价稳健性的工具.支持向量机方法已较成功地应用于信号与图像的滤波中,尤其Ricker子波核方法更适于地震勘探信号处理.通过考察Ricker子波核最小二乘支持向量回归(LS-SVR:least squares support vector regression)滤波方法的影响函数,可以证明该方法的稳健性较差,本文用加权方法改善该方法的稳健性.经过大量理论实验得到一种改进的权函数,使加权之后的方法具有比较理想的稳健性.进一步用这个权函数辅助的加权Ricker子波LS-SVR处理含噪的合成与实际地震记录,都得到较好的效果.由具有平方损失函数的LS-SVR信号处理系统的无界影响函数出发,本文所提出的权函数可以有效地应用于具有相似损失函数的处理过程,如消噪、信号检测、提高分辨率与预测等问题.展开更多
基金supported by the National Research and Development Project of China (2020YFB2008400).
文摘The hardness prediction model was established by support vector regression(SVR).In order to avoid exaggerating the contribution of very tiny alloying elements,a weighted fuzzy C-means(WFCM)algorithm was proposed for data clustering using improved Mahalanobis distance based on random forest importance values,which could play a full role of important features and avoid clustering center overlap.The samples were divided into two classes.The top 10 features of each class were selected to form two feature subsets for better performance of the model.The dimension and dispersion of features decreased in such feature subsets.Comparing four machine learning algorithms,SVR had the best performance and was chosen to modeling.The hyper-parameters of the SVR model were optimized by particle swarm optimization.The samples in validation set were classified according to minimum distance of sample to clustering centers,and then the SVR model trained by feature subset of corresponding class was used for prediction.Compared with the feature subset of original data set,the predicted values of model trained by feature subsets of classified samples by WFCM had higher correlation coefficient and lower root mean square error.It indicated that WFCM was an effective method to reduce the dispersion of features and improve the accuracy of model.
文摘随着电网公司代理购电业务稳步推进,代理购电业务体系逐步完善,精确的代理购电用户用电量预测为保障电力安全稳定供应奠定了基础。因此,文章构建自适应权重组合模型,将不同校核方法的校核结果进行权重分配,从而提升校核结果准确性。首先,构建预测业务偏差校核流程框架,确定代理购电预测业务校核流程。然后分别选取分位数映射法、增量变化法以及支持向量回归(support vector regression,SVR)对预测结果进行校核,得到同一纬度下的不同方法校核结果。最后,建立遗传算法-优劣解距离法(genetic algorithm-technique for order preference by similarity to ideal solution,GA-TOPSIS)模型针对校核结果进行准确性与稳定性双目标优化,选取不同校核方法的最优权重组合。测试结果表明在校核方法权重组合校正后,相较于初始预测值和单一校核方法校核后的结果,预测精度和准确度得到明显提升。
文摘除了信噪比、有效子波畸变等,稳健性(Robustness)也是度量滤波方法效果的一个重要的物理量,它刻画了滤波系统应对异常点值的能力.一般用影响函数作为评价稳健性的工具.支持向量机方法已较成功地应用于信号与图像的滤波中,尤其Ricker子波核方法更适于地震勘探信号处理.通过考察Ricker子波核最小二乘支持向量回归(LS-SVR:least squares support vector regression)滤波方法的影响函数,可以证明该方法的稳健性较差,本文用加权方法改善该方法的稳健性.经过大量理论实验得到一种改进的权函数,使加权之后的方法具有比较理想的稳健性.进一步用这个权函数辅助的加权Ricker子波LS-SVR处理含噪的合成与实际地震记录,都得到较好的效果.由具有平方损失函数的LS-SVR信号处理系统的无界影响函数出发,本文所提出的权函数可以有效地应用于具有相似损失函数的处理过程,如消噪、信号检测、提高分辨率与预测等问题.
