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A review on type-2 fuzzy neural networks for system identification

Authors
  • Tavoosi, Jafar1
  • Mohammadzadeh, Ardashir2
  • Jermsittiparsert, Kittisak3, 3, 4
  • 1 Ilam University,
  • 2 University of Bonab,
  • 3 Duy Tan University,
  • 4 MBA School, Henan University of Economics and Law, Henan, 450046 China
Type
Published Article
Journal
Soft Computing
Publisher
Springer-Verlag
Publication Date
Mar 09, 2021
Pages
1–16
Identifiers
DOI: 10.1007/s00500-021-05686-5
PMCID: PMC7941344
Source
PubMed Central
Keywords
License
Unknown

Abstract

In many engineering problems, the systems dynamics are uncertain, and then, the accurate dynamic modeling is required. Type-2 fuzzy neural networks (T2F-NNs) are extensively used in system identification problems, because of their strong estimation capability. In this paper, the application of T2F-NNs is reviewed and classified. First, an introduction to the principles of system identification, including how to extract data from a system, persistency of excitation, preprocessing of information and data, removal of outlier data, and sorting of data to learn the T2F-NNs, is presented. Then, various learning methods for structure and parameters of the T2F-NNs are reviewed and analyzed. A number of different T2F-NNs that have been used to system identification are reviewed, and their disadvantages and advantages are described. Also, their efficiency in different applications is reviewed. Finally, we will look at the horizon ahead in this issue and analyze its challenges.

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