Fuzzy logic and neuro-fuzzy modelling of diesel spray penetration

Lee, S.H., Howlett, R.J., Walters, S.D. and Crua, C. (2005) Fuzzy logic and neuro-fuzzy modelling of diesel spray penetration In: KES 2005, 14-16 September 2005, Melbourne, Australia.

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Abstract

This paper describes a comparative evaluation of two fuzzy-derived techniques for modelling fuel spray penetration in the cylinders of a diesel internal combustion engine. The first model is implemented using conventional fuzzy-based paradigm, where human expertise and operator knowledge were used to select the parameters for the system. The second model used an adaptive neuro-fuzzy inference system (ANFIS), where automatic adjustment of the system parameters is effected by a neural networks based on prior knowledge. Two engine operating parameters were used as inputs to the model, namely in-cylinder pressure and air density. Spray penetration length was modelled on the basis of these two inputs. The models derived using the two techniques were validated using test data that had not been used during training. The ANFIS model was shown to achieve an improved accuracy compared to a pure fuzzy model, based on conveniently selected parameters.

Item Type: Contribution to conference proceedings in the public domain ( Full Paper)
Additional Information: The final publication is available from springerlink.com
Subjects: G000 Computing and Mathematical Sciences > G600 Software Engineering
H000 Engineering > H300 Mechanical Engineering > H330 Automotive Engineering
G000 Computing and Mathematical Sciences > G700 Artificial Intelligence
Faculties: Faculty of Science and Engineering > School of Computing, Engineering and Mathematics > Engineering and Product Design Research > Automotive Engineering
Depositing User: Dr Cyril Crua
Date Deposited: 11 Mar 2010
Last Modified: 14 Oct 2014 13:22
URI: http://eprints.brighton.ac.uk/id/eprint/2194

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