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Fuzzy logic and neuro-fuzzy modelling of diesel spray penetration

By Shin Lee, R.J. Howlett, Simon Walters and Cyril Crua


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

Topics: G600 Software Engineering, H330 Automotive Engineering, G700 Artificial Intelligence
Publisher: Springer
Year: 2005
DOI identifier: 10.1007/11552451_88
OAI identifier:

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