| 基于混沌时序最小二乘支持向量机的汽油机瞬态空燃比预测模型研究 |
| |
| 引用本文： | 徐东辉,李岳林,雷鸣,何剑锋,吴钢,解福泉.基于混沌时序最小二乘支持向量机的汽油机瞬态空燃比预测模型研究[J].车用发动机,2015(2):13-17,22. |
| |
| 作者姓名： | 徐东辉 李岳林 雷鸣 何剑锋 吴钢 解福泉 |
| |
| 作者单位： | 1. 宜春学院物理科学与工程技术学院,江西宜春,336000;2. 长沙理工大学汽车与机械工程学院,湖南长沙,410076;3. 长沙理工大学汽车与机械工程学院，湖南长沙 410076; 河南交通职业技术学院，河南郑州 450005 |
| |
| 基金项目： | 国家自然科学基金项目(51406017)；国家自然科学基金项目(51176014)；高等学校博士学科点专项科研基金项目(20104316110002)；河南省交通厅科研项目(2012PII10)；工程车辆轻量化与可靠性技术湖南省高校重点实验室基金资助项目 |
| |
| 摘 要： | 针对由氧传感器构成的瞬态空燃比反馈控制系统无法满足实时性要求的问题,提出了基于混沌时序最小二乘支持向量机(LS-SVM)的瞬态空燃比预测模型。对试验采集到的一维空燃比数据利用相空间重构技术构造多维空间数据,恢复空燃比时间序列的多维非线性特性,然后采用LS-SVM对重构后的数据进行训练及预测,得出预测结果。仿真结果表明:与Elman神经网络预测模型及前馈BP神经网络预测模型相比较,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。
|
| 关 键 词： | 空燃比 相空间重构 瞬态工况 支持向量机 预测模型 |
Prediction Model of Transient Air-fuel Ratio for Gasoline Engine Based on Chaos Least Square Support Vector Machine |
| |
| XU Dong-hui,LI Yue-lin,LEI Ming,HE Jian-feng,WU Gang,XIE Fu-quan.Prediction Model of Transient Air-fuel Ratio for Gasoline Engine Based on Chaos Least Square Support Vector Machine[J].Vehicle Engine,2015(2):13-17,22. |
| |
| Authors: | XU Dong-hui LI Yue-lin LEI Ming HE Jian-feng WU Gang XIE Fu-quan |
| |
| Institution: | XU Dong-hui;LI Yue-lin;LEI Ming;HE Jian-feng;WU Gang;XIE Fu-quan;Physical Science and Engineering College of Yichun University;School of Automotive and Mechanical Engineering,Changsha University of Science and Technology;Henan Communications Vocational and Technical College; |
| |
| Abstract: | For the problem that the feedback control system of transient air‐fuel ratio with oxygen sensor could not realize the real‐time demand ,the prediction model of chaos least square support vector machine was put forward .The multi‐dimensional space data were constructed with the collected test data ,the multi‐dimensional non‐linear characteristics of air‐fuel ratio time series were restored ,the reconstructed data were trained with LS‐SVM and the prediction results were acquired .The results show that the chaos LS‐SVM prediction model has the non‐linear prediction ability and can improve the prediction accuracy of air‐fuel ratio effectively compared with the Elman and BP network model . |
| |
| Keywords: | air-fuel ratio phase space reconstruction transient condition support vector machine (SVM ) prediction model |
| 本文献已被 CNKI 万方数据 等数据库收录！ |
