汽油机瞬态空燃比的混沌时序LS-SVM预测研究  

Study of chaotic time series LS-SVM prediction of gasoline engine transient air-fuel ratio

作　　者：徐东辉[1,2] 代冀阳[3] 

机构地区：[1]宜春学院物理科学与工程技术学院,江西宜春336000 [2]长沙理工大学工程车辆轻量化与可靠性技术湖南省高校重点实验室,湖南长沙410076 [3]南昌航空大学信息工程学院,江西南昌330063

出　　处：《合肥工业大学学报（自然科学版）》2015年第11期1458-1462,共5页Journal of Hefei University of Technology：Natural Science

基　　金：国家自然科学基金资助项目(51406017;51176014);高等学校博士学科点专项科研基金资助项目(20104316110002);江西省科技支撑计划资助项目(20151BBE50108);河南省交通厅科研资助项目(2012PII10);工程车辆轻量化与可靠性技术湖南省高校重点实验室(长沙理工大学)开放基金资助项目(2013kfjj02)

摘　　要：在汽油机瞬态空燃比反馈控制过程中,氧传感器存在传输时滞,不能快速反馈汽油机瞬态空燃比真实值,无法满足瞬态空燃比反馈控制的实时性要求。文章提出了汽油机瞬态空燃比的混沌时序LS-SVM(最小二乘支持向量机)预测模型,采用相空间重构技术对原始数据进行重构,达到恢复汽油机瞬态空燃比时间序列的多维空间非线性特性目的,最后利用LS-SVM进行训练及预测,得到空燃比预测结果。仿真结果表明,与Elman网络及前馈BP网络相比,混沌时序LS-SVM预测模型具有更强的非线性预测能力,能够有效地提高瞬态空燃比的预测精度,为瞬态空燃比反馈控制的成功实行提供了有力的依据。In the process of feedback control of gasoline engine transient air-fuel ratio, the oxygen sensor has transmission delay and can not feed back the true value of gasoline engine transient air-fuel ratio quickly, thus failing in real-time control of transient air-fuel ratio. In this paper, the chaotic time series least squares-support vector machine（LS-SVM） prediction model of the gasoline engine transient air-fuel ratio is proposed. First, the original data are reconstructed by using phase-space reconstruction technique so as to recover the multidimensional nonlinear characteristics of time sequence of gasoline engine transient air-fuel ratio. Then LS-SVM is applied to training and identifying the reconstructed data. Finally, the air-fuel ratio identification results are obtained. The simulation results show that compared with the Elman neural network and feedforward BP neural network prediction models, the chaotic time series LS-SVM prediction model has stronger nonlinear prediction capability, and it can improve the prediction precision of transient air-fuel ratio effectively. This study can provide a basis for precise feedback control of transient air-fuel ratio.

关 键 词：瞬态工况 空燃比 LS-SVM预测模型 相空间重构 预测 

分 类 号：U464.171[机械工程—车辆工程]