Best multiple non-linear model factors for knock engine (SI) by using ANFIS
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...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
2014
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| Subjects: | |
| Online Access: | http://repo.uum.edu.my/12156/1/1358-6487-1-PB.pdf http://repo.uum.edu.my/12156/ http://www.ajouronline.com/index.php?journal=AJAS&page=index |
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| Summary: | 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. |
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