Title :
The Analysis of Sewers Inflammable Gas Based on PSO-SVR
Author :
Wang Hong-qi ; Cheng Xin-Wen ; Jiang Hua-Long
Author_Institution :
Sch. of Comput. Sci., Sichuan Univ. of Sci. & Eng., Zigong, China
Abstract :
Due to the non-liner, poor selectivity and cross-sensitivity of the combustible gas in the sewer, an analysis prediction model of the combustible gas in the sewer has been established based on the PSO-SVR machine, the model has introduced a new particle swarm algorithm to support the vector regression machine so that it can optimize the important parameters, realizing the automatic determination of parameters of the SVR machine, and be used for quantitative analysis of combustible gas in the sewer. The simulation results show that the model of the combustible gas in the sewer based on PSO-SVR machine is superior to the compared SVR model and it has better generalization performance and higher prediction accuracy.
Keywords :
gas sensors; particle swarm optimisation; regression analysis; support vector machines; PSO-SVR machine; combustible gas; gas detection; particle swarm optimisation; prediction model; sewers inflammable gas analysis; support vector regression; Accuracy; Analytical models; Data models; Educational institutions; Particle swarm optimization; Predictive models; Support vector machines; Inflammable Gas; Particle Swarm Optimization (PSO); Prediction model; Support Vector Regression;
Conference_Titel :
Instrumentation, Measurement, Computer, Communication and Control (IMCCC), 2013 Third International Conference on
Conference_Location :
Shenyang
DOI :
10.1109/IMCCC.2013.134
