Fuzzy Logic and Neuro-fuzzy Modelling of Diesel Spray Penetration

: The aim of this study was to demonstrate the effectiveness of an adaptive neuro-fuzzy inference system (ANFIS) for the prediction of diesel spray penetration length in the cylinder of a diesel internal combustion engine. The technique involved extraction of necessary representative features from a collection of raw image data. A comparative evaluation of two fuzzy-derived techniques for modelling fuel spray penetration is described. The first model was implemented using a 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 was effected by a neural network 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...

• Type: Conference
• Year: 2005
• Where: KES
• Authors: Shaun H. Lee, Robert J. Howlett, Simon D. Walters, Cyril Crua
• Keywords: Adaptive Neuro-fuzzy Inference, KES 2005, Spray Penetration, Spray Penetration Length