【摘要】：針對甲烷氣體定量分析過程中,傳統(tǒng)SVM模型預測精度低、收斂速度慢等問題,提出了一種基于改進PSO算法的SVM回歸模型。該模型在傳統(tǒng)PSO算法尋優(yōu)的基礎(chǔ)上,引入動量項的同時增加隨機粒子個體極值的追隨因子,使粒子不僅追隨全局最優(yōu)解和局部最優(yōu)解,還跟隨種群中任一粒子的個體極值,使得尋優(yōu)算法后期收斂速度較快,不易陷入局部最小值。實驗中,對0~5.05%濃度的25組標準甲烷樣氣進行建模分析,并與傳統(tǒng)PSO算法尋優(yōu)模型和Grid搜索法尋優(yōu)模型進行對比。結(jié)果表明,采用改進PSO算法建立的SVM回歸模型均方根誤差小,收斂速度快。
[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.
【作者單位】： 中國計量大學機電工程學院;
【基金】：浙江省大學生科技創(chuàng)新活動計劃暨新苗人才計劃項目(省級)(2016R409)
【分類號】：O212.1;TD712.5
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本文編號：2318343