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Neural Prediction in Industry: Increasing Reliability through Use of Confidence Measures and Model Combination

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Practical Applications of Computational Intelligence Techniques

Part of the book series: International Series in Intelligent Technologies ((ISIT,volume 16))

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Abstract

This chapter describes the application of neural networks to the prediction of “paper curl”, an important quality metric in the papermaking industry. In particular we address the issue of reliability in neural network training and prediction. Model combination is used to compensate for the limitations of non-linear optimization algorithms used for neural network training. In addition, confidence measures are used to characterize prediction uncertainty. Reliability enhancement though model combination enables training to be automated. The provision of a confidence measure along with the prediction facilitates the user in knowing whether to trust the prediction or not.

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Authors
  1. P. J. Edwards
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  2. G. Papadopoulos
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  3. A. F. Murray
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Editor information

Editors and Affiliations

  1. University of South Australia, Adelaide, Australia

    Lakhmi Jain

  2. University of London, London, UK

    Philippe De Wilde

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© 2001 Springer Science+Business Media New York

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Edwards, P.J., Papadopoulos, G., Murray, A.F. (2001). Neural Prediction in Industry: Increasing Reliability through Use of Confidence Measures and Model Combination. In: Jain, L., De Wilde, P. (eds) Practical Applications of Computational Intelligence Techniques. International Series in Intelligent Technologies, vol 16. Springer, Dordrecht. https://doi.org/10.1007/978-94-010-0678-1_5

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  • DOI: https://doi.org/10.1007/978-94-010-0678-1_5

  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-94-010-3868-3

  • Online ISBN: 978-94-010-0678-1

  • eBook Packages: Springer Book Archive

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