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
在现有的温度位移检测方法中,绝大部分仅适用于单变量测量,即使有能够同时测量两个变量的方法,测量范围也较窄,无法解决一些高温、腐蚀环境中大范围测量的问题。因此,本文提出了一种基于电涡流传感器的温度位移智能检测方法。首先,基于...在现有的温度位移检测方法中,绝大部分仅适用于单变量测量,即使有能够同时测量两个变量的方法,测量范围也较窄,无法解决一些高温、腐蚀环境中大范围测量的问题。因此,本文提出了一种基于电涡流传感器的温度位移智能检测方法。首先,基于长短期记忆网络(Long Short Term Memory Networks,LSTM)建立电涡流传感器探头线圈的等效电感L、激励频率f、品质因数Q与被测对象的位移x和温度T的模型,然后利用离线数据对该模型进行训练,再利用训练好的模型对位移x和温度T实现在线测量。最后利用仿真对该温度和位移检测方法的有效性进行验证,实验结果表明,与传统的BP、RBF、MOSVR相比,本文方法可以有效地对被测对象的位移x和温度T实现同时检测,并且优于其他方法。同时本文将该检测方法在单片机系统上进行了实现,以验证其解决实际工程问题的有效性。展开更多
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.
文摘在现有的温度位移检测方法中,绝大部分仅适用于单变量测量,即使有能够同时测量两个变量的方法,测量范围也较窄,无法解决一些高温、腐蚀环境中大范围测量的问题。因此,本文提出了一种基于电涡流传感器的温度位移智能检测方法。首先,基于长短期记忆网络(Long Short Term Memory Networks,LSTM)建立电涡流传感器探头线圈的等效电感L、激励频率f、品质因数Q与被测对象的位移x和温度T的模型,然后利用离线数据对该模型进行训练,再利用训练好的模型对位移x和温度T实现在线测量。最后利用仿真对该温度和位移检测方法的有效性进行验证,实验结果表明,与传统的BP、RBF、MOSVR相比,本文方法可以有效地对被测对象的位移x和温度T实现同时检测,并且优于其他方法。同时本文将该检测方法在单片机系统上进行了实现,以验证其解决实际工程问题的有效性。
