Point-wise confidence interval estimation by neural networks: A comparative study based on automotive engine calibration.


In developing neural network techniques for real world applications it is still very rare to see estimates of confidence placed on the neural network predictions. This is a major deficiency, especially in safety-critical systems. In this paper we explore three distinct methods of producing point-wise confidence intervals using neural networks. We compare and contrast Bayesian, Gaussian Process and Predictive error bars evaluated on real data. The problem domain is concerned with the calibration of a real automotive engine management system for both air-fuel ratio determination and on-line ignition timing. This problem requires real-time control and is a good candidate for exploring the use of confidence predictions due to its safety-critical nature.

Publication DOI: https://doi.org/10.1007/s005210050009
Divisions: College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
Additional Information: The original publication is available at www.springerlink.com
Uncontrolled Keywords: neural network,safety-critical systems,Gaussian Process,Predictive error,automotive engine management
Publication ISSN: 1433-3058
Last Modified: 03 Jun 2024 07:07
Date Deposited: 21 Sep 2009 15:16
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Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 1999-03
Authors: Lowe, David
Zapart, Krzysztof



Version: Published Version

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