Preface	6
	Contents	9
	About the Authors	11
	1 Artificial Intelligence for Modeling and Control of Nonlinear Phenomena in Internal Combustion Engines	13
	1.1 Neural Networks Architectures for Engine Applications	13
	1.2 Use of ANNs for Modeling and Control of Internal Combustion Engines	17
	1.2.1 Air–Fuel Ratio Prediction and Control	17
	1.2.2 Use of Neural Networks to Predict Combustion Pressure Parameters	25
	References	29
	2 Non-interfering Diagnostics for the Study of Thermo-Fluid Dynamic Processes	32
	2.1 Air Motion	33
	2.2 Mixture Preparation	34
	2.3 Combustion	37
	2.4 Pollutants Formation	41
	References	42
	3 Modeling of Particle Size Distribution at the Exhaust of Internal Combustion Engines	44
	3.1 Particulate Matter Emissions in Engines	45
	3.2 Real-Time Prediction of Particle Sizing at the Exhaust of a Diesel Engine	51
	References	55
	4 Diagnosis and Control of Engine Combustion Using Vibration Signals	58
	References	65
	5 Use of in-Cylinder Pressure and Learning Circuits for Engine Modeling and Control	66
	5.1 Cylinder Pressure Analysis and Extraction of Parameters for Engine Combustion Diagnosis and Control	67
	5.2 Use of Learning Algorithms in Pressure-Based Engine Controls	70
	5.3 Use of Combustion Pressure Signal for Cylinder Air Charge Estimation	70
	5.4 Engine Knock Detection and Control Using in-Cylinder Pressure Measurement	75
	References	80
	6 Identification and Compensation of Nonlinear Phenomena in Gasoline Direct Injection Process	83
	References	87
	Index	88