Research Journal of Applied Sciences

Year: 2014
Volume: 9
Issue: 12
Page No. 1059 - 1063


Hao, P., 2014. An improved back-propagation neural network algorithm. Applied Mech. Mater., 556-562: 4586-4590.
Direct Link  |  

Kruglov, V.V., M.I. Dli and G.R. Yu, 2000. Fuzzy Logic and Artificial Neural Networks. Fizmatlit, Moscow, Pages: 224.

Makushin, A.A., E.V. Zubkov and A.N. Ilyuhin, 2009. Application of fuzzy logic for modeling the internal combustion engine test modes. Assembly Mech. Instrument Eng., 8: 39-44.

Shatnawi, Y. and M. Al-Khassaweneh, 2014. Fault diagnosis in internal combustion engines using extension neural network. IEEE Trans. Ind. Electr., 61: 1434-1443.
CrossRef  |  

Stefanovsky, B.S., E.A. Skobtsev and E.K. Korsi, 1972. [Tests of Internal Combustion Engines]. Higher Education, Moscow, Pages: 368.

Yao, Z.T. and H.X. Pan, 2014. Engine fault diagnosis based on improved BP neural network with conjugate gradient. Applied Mech. Mater., 536-537: 296-299.
Direct Link  |  

Zhou, M.L., J.C. Wang and Y.P. Li, 2014. Automobile engine fault diagnosis and prediction system. Adv. Mater. Res., 1008-1009: 641-664.
Direct Link  |  

Zhou, Q., A. Gullitti, J. Xiao and Y. Huang, 2008. Neural network-based modeling and optimization for effective vehicle emission testing and engine calibration. Chem. Eng. Communi., 195: 706-720.
CrossRef  |  Direct Link  |  

Zubkov, E.V. and L.A. Galiullin, 2011. Hybrid neural network for the adjustment of fuzzy systems when simulating tests of internal combustion engines. Russian Eng. Res., 31: 439-443.
CrossRef  |  Direct Link  |  

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