Artificial Neural Network Applications in the Calibration of Engines von Turkson, Richard Fiifi; Yan, Fuwu; Ali, Mohamed Kamal Ahmed: New (2016)

Beschreibung

Bestandsnummer des Verk�ufers 26394736492

Bibliografische Details

Titel: Artificial Neural Network Applications in the Calibration of Engines
Verlag: LAP LAMBERT Academic Publishing
Erscheinungsjahr: 2016
Sprache: Englisch
ISBN-10: 3659956619
ISBN-13: 9783659956614
Einband: Softcover
Zustand: New

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Inhaltsangabe

Emissions legislation has become progressively tighter, making the development of new internal combustion engines very challenging. New engine technologies for complying with these regulations introduce an exponential dependency between the number of test combinations required for obtaining optimum results and the time and cost outlays. This makes the calibration task very expensive and virtually impossible to carry out. The potential use of trained neural networks in combination with Design of Experiments (DoE) methods for engine calibration has been a subject of research activities in recent times. The purpose of this book is to give an overview of the various applications of neural networks in the calibration of spark-ignition engines. The identified and discussed applications include system identification for rapid prototyping, virtual sensing, use of neural networks as look-up table surrogates, emerging control strategies and On-Board Diagnostic (OBD) applications. The demerits of neural networks, future possibilities and alternatives were also discussed.

Rese�a del editor

Emissions legislation has become progressively tighter, making the development of new internal combustion engines very challenging. New engine technologies for complying with these regulations introduce an exponential dependency between the number of test combinations required for obtaining optimum results and the time and cost outlays. This makes the calibration task very expensive and virtually impossible to carry out. The potential use of trained neural networks in combination with Design of Experiments (DoE) methods for engine calibration has been a subject of research activities in recent times. The purpose of this book is to give an overview of the various applications of neural networks in the calibration of spark-ignition engines. The identified and discussed applications include system identification for rapid prototyping, virtual sensing, use of neural networks as look-up table surrogates, emerging control strategies and On-Board Diagnostic (OBD) applications. The demerits of neural networks, future possibilities and alternatives were also discussed.

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