合肥工业大学学报（自然科学版）
閤肥工業大學學報（自然科學版）
합비공업대학학보（자연과학판）
Journal of Hefei University of Technology (Natural Science)
2015年
11期
1458-1462
,共5页
瞬态工况%空燃比%LS-SVM预测模型%相空间重构%预测
瞬態工況%空燃比%LS-SVM預測模型%相空間重構%預測
순태공황%공연비%LS-SVM예측모형%상공간중구%예측
transient condition%air-fuel ratio%least squares-support vector machine(LS-SVM ) predic-tion model%phase-space reconstruction%prediction
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。
在汽油機瞬態空燃比反饋控製過程中，氧傳感器存在傳輸時滯，不能快速反饋汽油機瞬態空燃比真實值，無法滿足瞬態空燃比反饋控製的實時性要求。文章提齣瞭汽油機瞬態空燃比的混沌時序LS‐SVM （最小二乘支持嚮量機）預測模型，採用相空間重構技術對原始數據進行重構，達到恢複汽油機瞬態空燃比時間序列的多維空間非線性特性目的，最後利用LS‐SVM 進行訓練及預測，得到空燃比預測結果。倣真結果錶明，與Elman網絡及前饋BP網絡相比，混沌時序LS‐SVM預測模型具有更彊的非線性預測能力，能夠有效地提高瞬態空燃比的預測精度，為瞬態空燃比反饋控製的成功實行提供瞭有力的依據。
재기유궤순태공연비반궤공제과정중，양전감기존재전수시체，불능쾌속반궤기유궤순태공연비진실치，무법만족순태공연비반궤공제적실시성요구。문장제출료기유궤순태공연비적혼돈시서LS‐SVM （최소이승지지향량궤）예측모형，채용상공간중구기술대원시수거진행중구，체도회복기유궤순태공연비시간서렬적다유공간비선성특성목적，최후이용LS‐SVM 진행훈련급예측，득도공연비예측결과。방진결과표명，여Elman망락급전궤BP망락상비，혼돈시서LS‐SVM예측모형구유경강적비선성예측능력，능구유효지제고순태공연비적예측정도，위순태공연비반궤공제적성공실행제공료유력적의거。
In the process of feedback control of gasoline engine transient air‐fuel ratio ,the oxygen sen‐sor has transmission delay and can not feed back the true value of gasoline engine transient air‐fuel ra‐tio 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 transi‐ent air‐fuel ratio is proposed .First ,the original data are reconstructed by using phase‐space recon‐struction 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 recon‐structed data . Finally , the air‐fuel ratio identification results are obtained . T he 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 pro‐vide a basis for precise feedback control of transient air‐fuel ratio .
