Accuracy Comparison of ARX and ANFIS Model of PM Brake Lining Wear Behavior
暂无分享,去创建一个
Ilyas Istif | Ovun Isin | Erdem Uzunsoy | Deniz Uzunsoy | Erdem Uzunsoy | Ovun Isin | Ilyas Istif | D. Uzunsoy | O. Isin | E. Uzunsoy
[1] Erdem Uzunsoy,et al. Non-Linear Modelling of PM Brake Lining Wear Behaviour , 2012 .
[2] Lennart Ljung,et al. System Identification: Theory for the User , 1987 .
[3] T. S. Mahmoud,et al. Prediction of wear behaviour of A356/SiCp MMCs using neural networks , 2009 .
[4] Ho Jang,et al. Effects of ingredients on tribological characteristics of a brake lining: an experimental case study , 2005 .
[5] Chuen-Tsai Sun,et al. Neuro-fuzzy modeling and control , 1995, Proc. IEEE.
[6] M. Bahrololoom,et al. Prediction of wear behaviors of nickel free stainless steel–hydroxyapatite bio-composites using artificial neural network , 2010 .
[7] Huy Nguyen,et al. A neural fuzzy approach to modeling the thermal behavior of power transformers , 2007 .
[8] Gholamhassan Najafi,et al. Diesel engine performance and exhaust emission analysis using waste cooking biodiesel fuel with an artificial neural network , 2009 .
[9] Shauna L. Hallmark,et al. Neuro-Fuzzy Approach to Predictive Modeling of Emissions from Biodiesel Powered Transit Buses , 2012 .
[10] Yafei Lu,et al. A combinatorial approach for automotive friction materials: Effects of ingredients on friction performance , 2006 .
[11] Dragan Aleksendrić,et al. Neural network prediction of brake friction materials wear , 2010 .
[12] Talal Yusaf,et al. Engine performance and emission analysis of LPG-SI engine with the aid of artificial neural network , 2011 .
[13] Murad Samhouri,et al. The effect of boost pressure on the performance characteristics of a diesel engine: A neuro-fuzzy approach , 2009 .
[14] Dafeng Tian,et al. Study of CWS/Diesel Dual Fuel Engine Emissions by Means of RBF Neural Network , 2010, 2010 Asia-Pacific Power and Energy Engineering Conference.
[15] N. El-Tayeb,et al. On the dry and wet sliding performance of potentially new frictional brake pad materials for automotive industry , 2009 .
[16] D. Uzunsoy,et al. Contribution of MoS2 additives to the Microstructure and Properties of PM Copper Based Brake Material , 2009 .
[17] H. Ouyang,et al. Wear prediction of friction material and brake squeal using the finite element method , 2008 .
[18] B. F. Yousif,et al. CNG-diesel engine performance and exhaust emission analysis with the aid of artificial neural network , 2010 .
[19] Soteris A. Kalogirou,et al. Artificial intelligence for the modeling and control of combustion processes: a review , 2003 .
[20] Vishy Karri,et al. Predictive models for emission of hydrogen powered car using various artificial intelligent tools , 2009, Neural Computing and Applications.
[21] N. Ravikumar,et al. Prediction of wear coefficient of Al6061–TiO2 composites , 2005 .
[22] J.-S.R. Jang,et al. Input selection for ANFIS learning , 1996, Proceedings of IEEE 5th International Fuzzy Systems.
[23] A. Kurt,et al. The effect of Al2O3 on the friction performance of automotive brake friction materials , 2007 .