Paper Title:
Research in Elman Neural Network for AFR Model of Automotive Engine
  Abstract

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%.

  Info
Periodical
Advanced Materials Research (Volumes 204-210)
Edited by
Helen Zhang, Gang Shen and David Jin
Pages
755-759
DOI
10.4028/www.scientific.net/AMR.204-210.755
Citation
Y. H. Bu, "Research in Elman Neural Network for AFR Model of Automotive Engine", Advanced Materials Research, Vols. 204-210, pp. 755-759, 2011
Online since
February 2011
Authors
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Price
$32.00
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