Best Multiple Non-linear Model Factors for knock Engine (SI) by using ANFIS


  • Azher Razzaq Hadi Witwit University Utara Malaysia(UUM)
  • Azman Yasin
  • Azman Yasin
  • Horizon Gitano
  • Horizon Gitano
  • Mohammed Ismael Mahmood
  • Mohammed Ismael Mahmood


Knocking, ANFIS, linear regression, Throttle position sensor (TPS), Revolution per minute (RPM).


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.



J. B. Heywood, Internal combustion engine fundamentals vol. 930: Mcgraw-hill New York, 1988.

K. M. Chun, "Characterization of knock and prediction of its onset in a spark-ignition engine," Massachusetts Institute of Technology, 1988.

G. Konig and C. Sheppard, "End–gas auto–ignition and knock in a spark ignition engine," Fuel, vol. 2013, pp. 09-12, 1990.


R. Barton, S. Lestz, and L. Duke, "Knock intensity as a function of engine rate of pressure change," Training, vol. 2005, pp. 04-01, 1970.

C. V. Ferraro, M. Marzano, and P. Nuccio, "Knock-Limit Measurement in High-Speed S. I. Engines," Studies, vol. 2011, pp. 12-14, 1984.

W. Leppard, "Individual-cylinder knock occurrence and intensity in multicylinder engines," 1982.

M. B. Jain, M. K. Nigam, and P. C. Tiwari, "Curve fitting and regression line method based seasonal short term load forecasting," in Information and Communication Technologies (WICT), 2012 World Congress on, 2012, pp. 332-337.

H. Andrei, T. Ivanovici, G. Predusca, E. Diaconu, and P. C. Andrei, "Curve fitting method for modeling and analysis of photovoltaic cells characteristics," in Automation Quality and Testing Robotics (AQTR), 2012 IEEE International Conference on, 2012, pp. 307-312.

D. Tamhane, P. Wong, F. Aminzadeh, and M. Nikravesh, "Soft computing for intelligent reservoir characterization," in SPE Asia Pacific Conference on Integrated Modelling for Asset Management, 2000.

R. Ibatullin, N. Ibragimov, R. Khisamov, E. Podymov, and A. Shutov, "Application and method based on artificial intelligence for selection of structures and screening of technologies for enhanced oil recovery," in SPE/DOE Improved Oil Recovery Symposium, 2002.

W. W. Weiss, R. S. Balch, and B. A. Stubbs, "How artificial intelligence methods can forecast oil production," in SPE/DOE Improved Oil Recovery Symposium, 2002.

Y. Liu, B. Bai, Y. Li, J. Coste, and X. Guo, "Optimization design for conformance control based on profile modification treatments of multiple injectors in a reservoir," in International Oil and Gas Conference and Exhibition in China, 2000.




How to Cite

Witwit, A. R. H., Yasin, A., Yasin, A., Gitano, H., Gitano, H., Mahmood, M. I., & Mahmood, M. I. (2014). Best Multiple Non-linear Model Factors for knock Engine (SI) by using ANFIS. Asian Journal of Applied Sciences, 2(4). Retrieved from