Characterisation of model error for Charpy impact energy of heat treated steels using probabilistic reasoning and a Gaussian mixture model

Mahfouf, Mahdi and Yang, Yong Y. and Zhang, Qian (2009) Characterisation of model error for Charpy impact energy of heat treated steels using probabilistic reasoning and a Gaussian mixture model. In: Automation in Mining, Mineral and Metal Processing. pp. 225-230. (doi:https://doi.org/10.3182/20091014-3-CL-4011.00041) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

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Official URL
http://doi.org/10.3182/20091014-3-CL-4011.00041

Abstract

Data-driven modelling has gained much momentum recently, with modelling algorithms being evolved into more complex structures capable of dealing with highly non-linear multi-dimensional systems. However, it is widely accepted that data-driven models are typically obtained under the principle of error minimisation, with the assumption of normal error distribution. The latter assumption is often not valid in more complex modelling environments, leading to sub-optimal model predictions. In this paper, a new modelling strategy aimed at exploiting the rich information contained in the model error data using a Gaussian mixture model (GMM) is proposed. The GMM error model can provide a probability characterisation of the error distribution, which can then be used complementally with the original data model. This combination often produces improvements in prediction performances, as will be illustrated in the case study relating to the hybrid modelling of the Charpy impact energy of heat-treated steels

Item Type: Conference or workshop item (Paper)
Subjects: Q Science > Q Science (General) > Q335 Artificial intelligence
T Technology > TA Engineering (General). Civil engineering (General) > TA168 Systems engineering, cybernetics and intelligent systems
T Technology > TA Engineering (General). Civil engineering (General) > TA 403 Materials Science
Divisions: Faculties > Sciences > School of Engineering and Digital Arts > Instrumentation, Control and Embedded Systems
Depositing User: Qian Zhang
Date Deposited: 18 Sep 2015 15:55 UTC
Last Modified: 22 Sep 2015 08:16 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/50546 (The current URI for this page, for reference purposes)
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