TY - JOUR N2 - This paper proposes a soft sensing method of least squares support vector machine (LS-SVM) using temperature time series for gas flow measurements. A heater unit has been installed on the external wall of a pipeline to generate heat pulses. Dynamic temperature signals have been collected upstream of the heater unit. The temperature time series are the main secondary variables of soft sensing technique for estimating the flow rate. A LS-SVM model is proposed to construct a non-linear relation between the flow rate and temperature time series. To select its inputs, parameters of the measurement system are divided into three categories: blind, invalid and secondary variables. Then the kernel function parameters are optimized to improve estimation accuracy. The experiments have been conducted both in the single-pulse and multiple-pulse heating modes. The results show that estimations are acceptable. L1 - http://www.czasopisma.pan.pl/Content/90334/PDF/Journal10178-Volume%20XXII%20Issue3_06paper.pdf L2 - http://www.czasopisma.pan.pl/Content/90334 PY - 2015 IS - No 3 EP - 392 DO - 10.1515/mms-2015-0028 KW - gas flow KW - soft sensor KW - support vector machine KW - temperature time series A1 - Xu, Weiqing A1 - Fan, Zichuan A1 - Cai, Maolin A1 - Shi, Yan A1 - Tong, Xiaomeng A1 - Sun, Junpeng PB - Polish Academy of Sciences Committee on Metrology and Scientific Instrumentation VL - vol. 22 DA - 2015[2015.01.01 AD - 2015.12.31 AD] T1 - Soft Sensing Method Of LS-SVM Using Temperature Time Series For Gas Flow Measurements SP - 383 UR - http://www.czasopisma.pan.pl/dlibra/publication/edition/90334 T2 - Metrology and Measurement Systems ER -