Open AccessPrediction of maximum in-cylinder pressure by adaptive neuro-fuzzy inference system method
Author(s) -
Farzad Jaliliantabar,
Gholamhassan Najafi,
Rizalman Mamat,
Barat Ghobadian
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/788/1/012066
Subject(s) - adaptive neuro fuzzy inference system , inference system , cylinder , computer science , diesel engine , neuro fuzzy , fuzzy logic , fuzzy control system , engineering , artificial intelligence , automotive engineering , mechanical engineering
A widely used method to substituting expensive experimental method in order to optimizing different parameters of technological application of equipment is using of modelling these phenomena by intelligent techniques. Hence, in this paper, an ANFIS (adaptive neuro-fuzzy inference system architecture) model has been used to predict one of the most important of the diesel engine which is cylinder pressure. Measurement of this parameter requires expensive and time consuming methods. Therefore, application of the mathematical method to prediction of this parameter is necessary. The inputs of this model are injection time, engine speed and engine load. The testing performance of the proposed ANFIS model revealed a good predictive capacity to yield acceptable error measures with, R 2 =0.99 and MSE=6.8. This model is not developed based on complicated mathematical formula and is easy to use. The result of study recommends that the ANFIS model can be successfully used to perdition of cylinder pressure according to effective parameters.