为提高多变量、非线性和强耦合系统的动态特性和解耦能力,解决逆模型辨识问题,讨论了基于最小二乘支持向量机(least squares support vector machines,LS-SVM)的多变量逆系统解耦控制方法。通过分析LS-SVM的函数拟合特性,离线建立被控...为提高多变量、非线性和强耦合系统的动态特性和解耦能力,解决逆模型辨识问题,讨论了基于最小二乘支持向量机(least squares support vector machines,LS-SVM)的多变量逆系统解耦控制方法。通过分析LS-SVM的函数拟合特性,离线建立被控对象的非线性逆模型,将得到的逆模型直接串接在原对象之前,原系统被解耦成多个独立的单变量伪线性子系统。为克服直接逆模型的建模误差,提高系统鲁棒稳定性,提出了复合控制方法,其中直接逆模型作为前馈控制器,而用PID控制器作为反馈控制器。文中还分析了球磨机控制系统的特点,并进行了仿真控制研究,仿真结果表明该复合控制方法不依赖于系统的精确数学模型,且解耦能力强、鲁棒稳定性好、跟踪精度高。展开更多
电站燃煤锅炉是大气NOx污染的主要来源之一,建立有效的NOx排放模型是锅炉优化降低NOx的基础。针对热工过程变量之间的强相关和耦合性,利用偏最小二乘方法(partial least squares,PLS)对多工况实炉热态测试数据进行重要变量(variable imp...电站燃煤锅炉是大气NOx污染的主要来源之一,建立有效的NOx排放模型是锅炉优化降低NOx的基础。针对热工过程变量之间的强相关和耦合性,利用偏最小二乘方法(partial least squares,PLS)对多工况实炉热态测试数据进行重要变量(variable importance in projection,VIP)信息提取和变量选择(variable selection,VS),把最优的变量子集作为最小二乘支持向量机(least squares support vector machine,LSSVM)的输入,最终得到NOx排放的VS-LSSVM模型。最优的输入变量个数通过留一交叉验证法获取。并将该模型与其他建模方法进行对比,结果表明通过变量选择后建模可以降低模型的复杂度,提高模型的泛化能力。展开更多
Machine learning techniques are finding more and more applications in the field of load forecasting. A novel regression technique,called support vector machine (SVM),based on the statistical learning theory is applied...Machine learning techniques are finding more and more applications in the field of load forecasting. A novel regression technique,called support vector machine (SVM),based on the statistical learning theory is applied in this paper for the prediction of natural gas demands. Least squares support vector machine (LS-SVM) is a kind of SVM that has different cost function with respect to SVM. SVM is based on the principle of structure risk minimization as opposed to the principle of empirical risk minimization supported by conventional regression techniques. The prediction result shows that the prediction accuracy of SVM is better than that of neural network. Thus,SVM appears to be a very promising prediction tool. The software package NGPSLF based on SVM prediction has been put into practical business application.展开更多
文摘为提高多变量、非线性和强耦合系统的动态特性和解耦能力,解决逆模型辨识问题,讨论了基于最小二乘支持向量机(least squares support vector machines,LS-SVM)的多变量逆系统解耦控制方法。通过分析LS-SVM的函数拟合特性,离线建立被控对象的非线性逆模型,将得到的逆模型直接串接在原对象之前,原系统被解耦成多个独立的单变量伪线性子系统。为克服直接逆模型的建模误差,提高系统鲁棒稳定性,提出了复合控制方法,其中直接逆模型作为前馈控制器,而用PID控制器作为反馈控制器。文中还分析了球磨机控制系统的特点,并进行了仿真控制研究,仿真结果表明该复合控制方法不依赖于系统的精确数学模型,且解耦能力强、鲁棒稳定性好、跟踪精度高。
文摘电站燃煤锅炉是大气NOx污染的主要来源之一,建立有效的NOx排放模型是锅炉优化降低NOx的基础。针对热工过程变量之间的强相关和耦合性,利用偏最小二乘方法(partial least squares,PLS)对多工况实炉热态测试数据进行重要变量(variable importance in projection,VIP)信息提取和变量选择(variable selection,VS),把最优的变量子集作为最小二乘支持向量机(least squares support vector machine,LSSVM)的输入,最终得到NOx排放的VS-LSSVM模型。最优的输入变量个数通过留一交叉验证法获取。并将该模型与其他建模方法进行对比,结果表明通过变量选择后建模可以降低模型的复杂度,提高模型的泛化能力。
文摘Machine learning techniques are finding more and more applications in the field of load forecasting. A novel regression technique,called support vector machine (SVM),based on the statistical learning theory is applied in this paper for the prediction of natural gas demands. Least squares support vector machine (LS-SVM) is a kind of SVM that has different cost function with respect to SVM. SVM is based on the principle of structure risk minimization as opposed to the principle of empirical risk minimization supported by conventional regression techniques. The prediction result shows that the prediction accuracy of SVM is better than that of neural network. Thus,SVM appears to be a very promising prediction tool. The software package NGPSLF based on SVM prediction has been put into practical business application.
