传感技术学报Issue(8)：1262-1268,7.DOI:10.3969/j.issn.1004-1699.2015.08.027
基于自适应变异粒子群优化的SVM在混合气体分析中的应用
Application of Support Vector Machine Based on Adaptive Mutation Particle Swarm Optimization in Analysis of Gas Mixture
曲健 1陈红岩 1刘文贞 1张兵 1李志彬1
作者信息
- 1. 中国计量学院机电工程学院，杭州310018
- 折叠
摘要
Abstract
For the difficult in selecting parameter of SVM modeling,the data calculation excessive in infrared spec⁃troscopy,as well as crosstalk between gases and other issues in the quantitative analysis of mixed gas. A solution of adaptive mutation particle swarm optimization support vector machine was proposed. It was to establish the models of a multi-component mixture gases quantitative analysis based on infrared spectroscopy. Multi-component mixture gases are composed of CO,with the concentration range from 0.5%to 8%;CO2,with the concentration range from 3.6%to 12.5%;C3H8,with the concentration range from 200×10-6 to 3270×10-6.Use the Particle swarm optimization algorithm to optimize select the parameters in support vector machine modeling,and compare the support vector machine modeling parameters in genetic algorithm optimization. Experiments show that it takes 39.524 s for modeling and it takes 26.272 s with genetic algorithm;for the predict results of CO2 in independent modeling,the variance of PSO algorithms is 0.000 123 758,the variance of genetic algorithms is 2.149 52. In the case of modeling time slightly higher,the predict results were sig⁃nificantly lower than the variance of the genetic algorithm.关键词
传感器应用/支持向量机/粒子群优化/遗传算法/定量分析Key words
sensor application/SVM/particle swarm optimization/genetic algorithms/quantitative analysis分类
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曲健,陈红岩,刘文贞,张兵,李志彬..基于自适应变异粒子群优化的SVM在混合气体分析中的应用[J].传感技术学报,2015,(8):1262-1268,7.
