Virtual sensing of SI engines using recurrent neural networks

SAE Technical Papers

Volumn , Issue , 2006, Pages

  Kamat, Shivaram ^a   Diwanji, Vivek ^a   Smith, Jessy ^a   Javaherian, Hossein ^b   Madhavan, K P ^c  

^a TATA CONSULTANCY SERVICES   (India)

^b GENERAL MOTORS CORPORATION   (United States)

^c INDIAN INSTITUTE OF TECHNOLOGY BOMBAY   (India)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL EFFICIENCY; ENGINES; FAULT TOLERANCE; NETWORK ARCHITECTURE; STATE SPACE METHODS; SYSTEMS ANALYSIS;

ABSOLUTE PRESSURE; DESIGN AND OPERATIONS; ENGINE DIAGNOSTICS; FAULT TOLERANT CONTROL SYSTEMS; NETWORK TOPOLOGY; NONLINEAR STATE SPACE MODELS; REAL TIME RECURRENT LEARNING ALGORITHMS; RECURRENT NEURAL NETWORK (RNN);

RECURRENT NEURAL NETWORKS;

EID: 85072430497     PISSN: 01487191     EISSN: 26883627     Source Type: Journal    
DOI: 10.4271/2006-01-1348     Document Type: Conference Paper

References (9)

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2. Onder, C.H., and Geering H.P., "Model Based Multivariable Speed and Air-Fuel Ratio Control of a SI Engine" SAE Publication no. 930859.
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7. Lenz, Ulrich, and Schröder Dierk, "Air-Fuel Ratio Control for Direct Injecting Combustion Engines using Neural Networks" SAE Publication no. 981060.
8. Müller, Rainer, and Schneider Bernd, "Approximation and Control of the Engine Torque using Neural Networks" SAE Publication no. 2000-01-0929.
9. Javaherian, Hossein, Liu Derong, Zhang Yi, and Kovalenko Olesia, "Adaptive Critic Learning Techniques for Automotive Engine Control" Proceeding of the 2004 American Control Conference Boston, Massachusetts, pp 4066-4071, June 30-July 2, 2004.