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Best multiple non-linear model factors for knock engine (SI) by using ANFIS


Witwit, Azher Razzaq Hadi and Yasin, Azman and Gitano, Horizon and Mahmood, Mohammed Ismael (2014) Best multiple non-linear model factors for knock engine (SI) by using ANFIS. Asian Journal of Applied Sciences, 02 (04). pp. 464-470. ISSN 2321 – 0893

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Abstract

Knock Prediction in vehicles is an ideal problem for non-linear regression to deal with, which use many of the factors of information to predict another factor. Training data were collected through a test engine for the Malaysian Proton company and in various states of speed.Selected six influential factors on the knocking(Throttle Position Sensor(TPS),Temperature(TEMP),Revolution Per Minute(RPM),(TORQUE),Ignition Timing( IGN),Acceleration Position(AC_POS)), has been taking data for this study and then applied to a single cylinder,output factor (output variable) to be prediction factor is a knock.We compare the performance of resultant ANFIS and Linear regression to obtain results shows effectiveness ANFIS, as well as three factors were selected from six non-linear factors to get the best model by using Adaptive Neuro-Fuzzy Inference System (ANFIS).Experiments demonstrate that although soft computing methods are somewhat of tolerant of inaccurate inputs, cleaned data results in more robust models for practical problems.

Item Type: Article
Uncontrolled Keywords: Knocking, ANFIS, linear regression, Throttle position sensor (TPS), Revolution per minute (RPM)
Subjects: T Technology > TJ Mechanical engineering and machinery
Divisions: School of Computing
Depositing User: Dr. Azman Yasin
Date Deposited: 14 Sep 2014 03:56
Last Modified: 26 May 2016 03:22
URI: http://repo.uum.edu.my/id/eprint/12156

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