Multi-step predictive model of air fuel ratio for gasoline engine based on neural network

2006 ◽  
Author(s):  
Hou Zhixiang ◽  
Wu Yihu
Keyword(s):  
Neural Network ◽  
Predictive Model ◽  
Gasoline Engine ◽  
Fuel Ratio

Research in Elman Neural Network for AFR Model of Automotive Engine

2011 ◽  
Vol 204-210 ◽  
pp. 755-759
Author(s):  
Yu Hong Bu
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Gasoline Engine ◽  
Model Identification ◽  
Ratio Model ◽  
Power Performance ◽  
Elman Neural Network ◽  
Automotive Engine ◽  
Engine Model ◽  
Fuel Ratio

Air fuel ratio is a key index affecting power performance and fuel economy and exhaust emissions of the gasoline engine, whose accurate model is the foundation of accuracy air fuel ratio control. In the paper, at first, it has studied the Elman neural network (NN) simulation model of Air Fuel ratio physical model of automotive engine. Second, employing the SI-V8 in en-DYNA engine model as experimental device, the paper discussed the structure determination of Elman neural network; finally, it compared model identification performance between Elman and BP neural network. Experiment results show the generalization performance of neural network does not have a linear relationship to the neurons in hidden layer of Elman NN, and the air fuel ratio based on Elman neural network is better than the air fuel ratio model based on BP neural network. The average relative error of Elman NN air fuel ratio model is less than 0.5%, however, which of BP NN is more than 1%.

Neural Network Based Model Predictive Control of Gasoline Engine Air-Fuel Ratio

1996 ◽  
Vol 29 (1) ◽  
pp. 7784-7789
Author(s):  
B. Lennox ◽  
G.A. Montague ◽  
A.M. Frith ◽  
A.J. Beaumont
Keyword(s):  
Neural Network ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Gasoline Engine ◽  
Fuel Ratio

scholarly journals An Air Quality Predictive Model of Licang of Qingdao City Based on BP Neural Network

2013 ◽  
Vol 756-759 ◽  
pp. 3366-3371 ◽  
Author(s):  
Ruo Bo Xin ◽  
Zhi Fang Jiang ◽  
Ning Li ◽  
Lu Jian Hou
Keyword(s):  
Neural Network ◽  
Air Quality ◽  
Predictive Model ◽  
Bp Neural Network ◽  
Ambient Air ◽  
Monitoring Site ◽  
Ambient Air Quality ◽  
Bp Network ◽  
Air Quality Forecast ◽  
Qingdao City

In order to obtain high precision results of urban air quality forecast, we propose a short-term predictive model of air quality in this paper, which is on the basis of the ambient air quality monitoring data and relevant meteorological data of a monitoring site in Licang district of Qingdao city in recent three years. The predictive model is based on BP neural network and used to predict the ambient air quality in the next some day or within a certain period of hours. In the design of the predictive model, we apply LM algorithm, Simulated Annealing algorithm and Early Stopping algorithm into BP network, and use a reasonable method to extract the historical data of two years as the training samples, which are the main reasons why the prediction results are better both in speed and in accuracy. And when predicting within a certain period of hours, we also adopt an average and equivalent idea to reduce the error accuracy, which brings us good results.

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