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Erschienen in:
Adaptive Flux Observers and Rotor Speed Sensor Fault Detection in Induction Motors Kapitel lesen Erstes Kapitel lesen

2014 | OriginalPaper | Buchkapitel

19. A System Identification Framework for Modeling Complex Combustion Dynamics Using Support Vector Machines

verfasst von : Vijay Manikandan Janakiraman, XuanLong Nguyen, Jeff Sterniak, Dennis Assanis

Erschienen in: Informatics in Control, Automation and Robotics

Verlag: Springer International Publishing

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Abstract

Machine Learning is being widely applied to problems that are difficult to model using fundamental building blocks. However, the application of machine learning in powertrain modeling is not common because existing powertrain systems have been simple enough to model using simple physics. Also, black box models are yet to demonstrate sufficient robustness and stability features for widespread powertrain applications. However, with emergence of advanced technologies and complex systems in the automotive industry, obtaining a good physical model in a short time becomes a challenge and it becomes important to study alternatives. In this chapter, support vector machines (SVM) are used to obtain identification models for a gasoline homogeneous charge compression ignition (HCCI) engine. A machine learning framework is discussed that addresses several challenges for identification of the considered system that is nonlinear and whose region of stable operation is very narrow.

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Vorheriges Kapitel A Component-Oriented Model for Wastewater Pumping Plants
Fußnoten
1
Acknowledgements and disclaimer: This material is based upon work supported by the Department of Energy [National Energy Technology Laboratory] under Award Number(s) DE-EE0003533. This work is performed as a part of the ACCESS project consortium (Robert Bosch LLC, AVL Inc., Emitec Inc.) under the direction of PI Hakan Yilmaz, Robert Bosch, LLC. This report was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.
 
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Metadaten
Titel
A System Identification Framework for Modeling Complex Combustion Dynamics Using Support Vector Machines
verfasst von
Vijay Manikandan Janakiraman
XuanLong Nguyen
Jeff Sterniak
Dennis Assanis
Copyright-Jahr
2014
Buch
Informatics in Control, Automation and Robotics

Print ISBN: 978-3-319-03499-7

Electronic ISBN: 978-3-319-03500-0

Copyright-Jahr: 2014

DOI
https://doi.org/10.1007/978-3-319-03500-0_19

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