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

David Lowe, Krzysztof Zapart

Research output: Contribution to journalArticlepeer-review

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.
Original languageEnglish
Pages (from-to)77-85
Number of pages9
JournalNeural Computing and Applications
Volume8
Issue number1
DOIs
Publication statusPublished - Mar 1999

Bibliographical note

The original publication is available at www.springerlink.com

Keywords

  • neural network
  • safety-critical systems
  • Gaussian Process
  • Predictive error
  • automotive engine management

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