A constraint-based genetic algorithm for optimizing neural network architectures for detection of loss of coolant accidents of nuclear power plants

Tian, D., Deng, J., Vinod, G. et al. (2 more authors) (2018) A constraint-based genetic algorithm for optimizing neural network architectures for detection of loss of coolant accidents of nuclear power plants. Neurocomputing, 322. pp. 102-119. ISSN 0925-2312

Abstract

Metadata

Authors/Creators:
  • Tian, D.
  • Deng, J.
  • Vinod, G.
  • Santhosh, T.V.
  • Tawfik, H.
Copyright, Publisher and Additional Information: © 2018 Elsevier. This is an author produced version of a paper subsequently published in Neurocomputing. 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: Multilayer perceptron; Genetic algorithms; Constraint satisfaction problems; Random search; Exhaustive search; Loss of coolant accidents of NPP
Dates:
  • Accepted: 7 September 2018
  • Published (online): 24 September 2018
  • Published: December 2018
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield)
Depositing User: Symplectic Sheffield
Date Deposited: 19 Sep 2019 14:06
Last Modified: 24 Sep 2019 00:39
Status: Published
Publisher: Elsevier
Refereed: Yes
Identification Number: https://doi.org/10.1016/j.neucom.2018.09.014

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