A neural network air-fuel ratio estimator
A neural network air-fuel ratio estimator
- Author(s):
- DOI: 10.1049/cp:19940127
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- Author(s): Source: International Conference on Control '94, 1994 p. 165 – 172
- Conference: International Conference on Control '94
- DOI: 10.1049/cp:19940127
- ISBN: 0 85296 610 5
- Location: Coventry, UK
- Conference date: 21-24 March 1994
- Format: PDF
The paper suggests that a cheap, reliable method of measuring or estimating engine Air-Fuel Ratio (AFR) is needed for effective control. The behaviour of the intake manifold, which is the main cause of the control problem, is discussed, and the use of neural networks for estimating AFR is suggested. The main features of such networks in system modelling are given and the training of two different networks using a simulator is described. The results of tests carried out on the trained networks are given and discussed, and it is concluded that such work deserves further research.
Inspec keywords: internal combustion engines; chemical variables control; learning (artificial intelligence); automobiles; transport computer control; neural nets
Subjects: Automobile industry; Neural computing techniques; Road-traffic system control; Control technology and theory (production); Civil and mechanical engineering computing; Neural nets (theory); Chemical variables control
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