SVM Based Regression Schemes for Instruments Fault Accommodation in Automotive Systems

  • Domenico Capriglione
  • Claudio Marrocco
  • Mario Molinara
  • Francesco Tortorella
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3617)

Abstract

The paper deals with the use of Support Vector Machines (SVMs) and performance comparisons with Artificial Neural Networks (ANNs) in software-based Instrument Fault Accommodation schemes. As an example, a real case study on an automotive systems is presented. The ANNs and SVMs regression capability are employed to accommodate faults that could occur on main sensors involved in the operating engine. The obtained results prove the good behaviour of both tools and similar performances have been achieved in terms of accuracy.

Keywords

Support Vector Machine Mean Absolute Error Sensor Fault Cross Validation Technique Prediction Phase 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Domenico Capriglione
    • 1
  • Claudio Marrocco
    • 1
  • Mario Molinara
    • 1
  • Francesco Tortorella
    • 1
  1. 1.Dipartimento di Automazione, Elettromagnetismo, Ingegneria dell’Informazione e Matematica IndustrialeUniversità degli Studi di CassinoCassinoItaly

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