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Steel processing using a Bayesian adaptive network

Lookup NU author(s): Tom Musicka, Professor Elaine Martin

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Abstract

Bayesian methods offer a sound mathematical foundation on which to optimise models, prevent overfitting of data and realise the incorporation of prior knowledge when building models. The standard Adaptive Network-based Fuzzy Inference System (ANFIS) is modified to enable the integration of Bayesian methodologies. The proposed approach is compared with linear partial least squares, neural networks and a standard gradient descent based ANFIS model for the prediction of a mechanical property in steel manufacturing.

Publication metadata

Author(s): Musicka T, Martin E, Morris J, Kitson P

Editor(s): McNulty, G.J.

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: Quality, Reliability and Maintenance (QRM)

Year of Conference: 2002

Pages: 195-198

Publisher: Wiley

Library holdings: Search Newcastle University Library for this item

ISBN: 9781860583698

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