Long-term learning for type-2 neural-fuzzy systems

Baraka, A. and Panoutsos, G. (2019) Long-term learning for type-2 neural-fuzzy systems. Fuzzy Sets and Systems. ISSN 0165-0114

Abstract

Metadata

Authors/Creators:
  • Baraka, A.
  • Panoutsos, G.
Copyright, Publisher and Additional Information: © 2019 Elsevier. This is an author produced version of a paper subsequently published in Fuzzy Sets and Systems. 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: Radial-basis-function neural fuzzy (RBF-NF) system; Interval-valued fuzzy logic system; Granular computing (GrC); Long-term learning; Incremental learning; Similarity measures for type-2 fuzzy sets
Dates:
  • Accepted: 22 December 2018
  • Published (online): 2 January 2019
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: 14 Jan 2019 10:44
Last Modified: 14 Jan 2019 10:44
Published Version: https://doi.org/10.1016/j.fss.2018.12.014
Status: Published online
Publisher: Elsevier
Refereed: Yes
Identification Number: https://doi.org/10.1016/j.fss.2018.12.014

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