关键词: 热导传感器, 温度特性校正, 支持向量机, 数据融合, 经典粒子群优化

Abstract: To eliminate the influence of ambient temperature on thermal sensor in gas detection, the authors put forward a new calibration method for sensor temperature characteristic based on data fusion and Canonical Particle Swarm Optimization-Support Vector Machine (CPSO-SVM). The method adopted SVM to fuse the data of sensor pair composed of a thermal sensor and a temperature sensor, and applied CPSO and the principle of Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) minimization of test samples set to tune the parameter vector of SVM. The experimental results of H2 detection show that the proposed method can effectively improve the temperature quality of thermal sensor, and realizes accurate detection of gas concentration.

Key words: thermal sensor, temperature characteristic calibration, Support Vector Machine (SVM), data fusion, Canonical Particle Swarm Optimization (CPSO)