Title :
Determining adjustment ranges for model-based approaches using support vector machines
Author :
Bazarsuren, Uzmee ; Knaak, Mirko ; Schaum, Steffen ; Gühmann, Clemens
Author_Institution :
Dept. of Electron. Meas. & Diagnostic Technol., Technische Univ. Berlin
Abstract :
Model-based methods, such as design of experiments (DoE), have become more and more established in recent years in optimizing control maps in engine ECUs from the aspects of ride comfort, fuel economy and emissions. As a result of the rising number of control parameters and ever shorter development times, the aim in this context is to develop automated intelligent setting strategies for test design. In doing so, it is imperative to find a range of settings at which the engine works safely (adjustment range). This paper presents a method for determining adjustment range limits in engine measurement for high dimensional parameter spaces using the support vector machines (SVM). SVMs are a relatively new method in machine learning and are applied to learn a hull that models the unknown, actual test space on the basis of measurement points
Keywords :
internal combustion engines; learning (artificial intelligence); support vector machines; control maps; control parameters; engine measurement; machine learning; model-based approaches; model-based methods; support vector machines; test design; Automation; Combustion; Electronic equipment testing; Engines; Mathematical model; Optimization methods; Pollution measurement; Space technology; Support vector machines; Temperature;
Conference_Titel :
Computer Aided Control System Design, 2006 IEEE International Conference on Control Applications, 2006 IEEE International Symposium on Intelligent Control, 2006 IEEE
Conference_Location :
Munich
Print_ISBN :
0-7803-9797-5
Electronic_ISBN :
0-7803-9797-5
DOI :
10.1109/CACSD-CCA-ISIC.2006.4776958