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

Lowe, David and Zapart, Krzysztof (1999). Point-wise confidence interval estimation by neural networks: A comparative study based on automotive engine calibration. Neural Computing and Applications, 8 (1), pp. 77-85.

Abstract

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: Engineering & Applied Sciences > Mathematics
Engineering & Applied 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
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Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
Published Date: 1999-03
Authors: Lowe, David
Zapart, Krzysztof

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