Transient Air-Fuel Ratio Estimation in Spark Ignition Engine Using Recurrent Neural Networks

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外文摘要：Neural networks is very useful in modeling processes for which mathematical modeling is difficult or impossible。 In the present work recurrent neural network (RNN) is used for air-fuel ratio (AFR) estimation in Spark Ignition (SI) Engine。 AFR estimation is difficult due to the nonlinearity and dynamic behavior in SI engines。 Additionally， delays in engine dynamics limit the performance of engine controller。 Estimating AFR a few steps in advance can help engine controller to take care of these。 RNN is trained using data from engine simulations in MATLAB/SIMULINK environment。 Uncorrelated signals were generated for training and validation。 It has been shown that recurrent neural network can predict engine simulations with reasonably good accuracy。

外文关键词：

air-fuel ratioair-fuel ratio estimationrecurrent neural network

作者：

Yanhong Zhang、Lifeng Xi、James Liu

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作者单位：

School of Computer Science and Information Technology, Zhejiang Wanli University, Ningbo, Zhejiang 315100, P.R. China

BorgWarner Automotive Components (Ningbo) Co.,Ltd, Ningbo, Zhejiang 315104, P.R. China

会议名称：

Knowledge-Based Intelligent Information and Engineering Systems pt.2: KES 2007 - WIRN 2007; Lecture Notes in Artificial Intelligence; 4692

会议地点：

Santiago de Compostela(ES);Santiago de Compostela(ES)

会议母体文献：

International Conference on Knowledge-Based Intelligent Information and; International Conference on Knowledge-Based Intelligent Information and Engineering Systems(KES 2007); Italian Workshop on Neural Networks; 20070912-14; 20070912-14; Santiago de Com

页码：

P.240-246

出版时间：

2007