基于改进PSO算法的SVM在甲烷测量中的应用
发布时间：2018-11-08 11:23
【摘要】：针对甲烷气体定量分析过程中,传统SVM模型预测精度低、收敛速度慢等问题,提出了一种基于改进PSO算法的SVM回归模型。该模型在传统PSO算法寻优的基础上,引入动量项的同时增加随机粒子个体极值的追随因子,使粒子不仅追随全局最优解和局部最优解,还跟随种群中任一粒子的个体极值,使得寻优算法后期收敛速度较快,不易陷入局部最小值。实验中,对0~5.05%浓度的25组标准甲烷样气进行建模分析,并与传统PSO算法寻优模型和Grid搜索法寻优模型进行对比。结果表明,采用改进PSO算法建立的SVM回归模型均方根误差小,收敛速度快。
[Abstract]:In order to solve the problems of low prediction accuracy and slow convergence rate in the process of methane gas quantitative analysis, a new SVM regression model based on improved PSO algorithm is proposed. On the basis of the traditional PSO algorithm, the momentum term is introduced and the following factor of individual extremum of random particle is added, so that the particle not only follows the global optimal solution and the local optimal solution, but also follows the individual extremum of any particle in the population. The convergence speed of the optimization algorithm is fast and it is not easy to fall into the local minimum. In the experiment, 25 groups of standard methane sample gas with 0 5. 05% concentration were modeled and analyzed, and compared with the traditional PSO algorithm and Grid search optimization model. The results show that the root-mean-square error of the SVM regression model based on the improved PSO algorithm is small and the convergence rate is fast.
【作者单位】： 中国计量大学机电工程学院;
【基金】：浙江省大学生科技创新活动计划暨新苗人才计划项目(省级)(2016R409)
【分类号】：O212.1;TD712.5
，
本文编号：2318344
[Abstract]:In order to solve the problems of low prediction accuracy and slow convergence rate in the process of methane gas quantitative analysis, a new SVM regression model based on improved PSO algorithm is proposed. On the basis of the traditional PSO algorithm, the momentum term is introduced and the following factor of individual extremum of random particle is added, so that the particle not only follows the global optimal solution and the local optimal solution, but also follows the individual extremum of any particle in the population. The convergence speed of the optimization algorithm is fast and it is not easy to fall into the local minimum. In the experiment, 25 groups of standard methane sample gas with 0 5. 05% concentration were modeled and analyzed, and compared with the traditional PSO algorithm and Grid search optimization model. The results show that the root-mean-square error of the SVM regression model based on the improved PSO algorithm is small and the convergence rate is fast.
【作者单位】： 中国计量大学机电工程学院;
【基金】：浙江省大学生科技创新活动计划暨新苗人才计划项目(省级)(2016R409)
【分类号】：O212.1;TD712.5
，
本文编号：2318344
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