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Nonlinear Data Classification and Applications to Vehicle Calibration Identification

[+] Author Affiliations
Jing Wang, Yan Wang, Dimitar Filev, Lirjon Llusho

Ford Motor Company, Dearborn, MI

Paper No. DSCC2016-9631, pp. V001T12A001; 7 pages
doi:10.1115/DSCC2016-9631
From:
  • ASME 2016 Dynamic Systems and Control Conference
  • Volume 1: Advances in Control Design Methods, Nonlinear and Optimal Control, Robotics, and Wind Energy Systems; Aerospace Applications; Assistive and Rehabilitation Robotics; Assistive Robotics; Battery and Oil and Gas Systems; Bioengineering Applications; Biomedical and Neural Systems Modeling, Diagnostics and Healthcare; Control and Monitoring of Vibratory Systems; Diagnostics and Detection; Energy Harvesting; Estimation and Identification; Fuel Cells/Energy Storage; Intelligent Transportation
  • Minneapolis, Minnesota, USA, October 12–14, 2016
  • Conference Sponsors: Dynamic Systems and Control Division
  • ISBN: 978-0-7918-5069-5
  • Copyright © 2016 by ASME

abstract

In this paper, we consider the general problem of data classification, and focus on the development of suitable approaches for automotive calibration applications. Specifically, we propose two methods for nonlinear data classification: the first one is to find the smallest piecewise linear (PWL) region for a given data set and the second one is to construct piecewise quadratic (PWQ) boundary to separate two data sets . In both methods, the construction of the boundary curve is formulated as convex optimization problems that can be solved efficiently. Our approaches can incorporate prior information about data distribution and allow fixed structure of the decision boundary from different data sets with similar generating sources. We demonstrate the efficiency and effectiveness of the approaches with an application to calibration identification of vehicle rollover detection algorithm.

Copyright © 2016 by ASME
Topics: Vehicles , Calibration

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