Hybrid-modelling of compact tension energy in high strength pipeline steel using a Gaussian Mixture Model based error compensation

Zhang, G., Mahfouf, M., Abdulkareem, M. et al. (6 more authors) (2016) Hybrid-modelling of compact tension energy in high strength pipeline steel using a Gaussian Mixture Model based error compensation. Applied Soft Computing, 48. pp. 1-12. ISSN 1568-4946

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Authors/Creators:
Copyright, Publisher and Additional Information: © 2016 Elsevier. This is an author produced version of a paper subsequently published in Applied Soft Computing. Uploaded in accordance with the publisher's self-archiving policy. Article available under the terms of the CC-BY-NC-ND licence (https://creativecommons.org/licenses/by-nc-nd/4.0/)
Keywords: Pipeline; steel; Gaussian Mixture Model; fuzzy; Neural Networks; prediction
Dates:
  • Published: 28 June 2016
  • Accepted: 9 June 2016
  • Published (online): 28 June 2016
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield)
The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Mechanical Engineering (Sheffield)
Depositing User: Symplectic Sheffield
Date Deposited: 23 Aug 2016 12:24
Last Modified: 19 Jul 2017 10:21
Published Version: http://dx.doi.org/10.1016/j.asoc.2016.06.007
Status: Published
Publisher: Elsevier
Refereed: Yes
Identification Number: https://doi.org/10.1016/j.asoc.2016.06.007

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