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Fuzzy equations and Z-numbers for nonlinear systems control

Razvarz, S ; Sharif University of Technology | 2017

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  1. Type of Document: Article
  2. DOI: 10.1016/j.procs.2017.11.327
  3. Publisher: Elsevier B.V , 2017
  4. Abstract:
  5. Various systems with nonlinearity can be modeled by utilizing uncertain linear-in-parameter models. In this paper, the uncertain parameters are in the form of Z-number coefficients. Fuzzy equations are utilized to represent the models of the uncertain nonlinear systems. The solutions associated with fuzzy equations are considered to be controllers while the desired references are outputs. The existence condition associated with the solution is laid down. Two various structure of neural networks are applied for approximating solutions of fuzzy equations with Z-number coefficients. The suggested techniques are validated by implementing an example. © 2018 The Authors. Published by Elsevier B.V
  6. Keywords:
  7. Fuzzy equation ; Neural network ; Computation theory ; Control nonlinearities ; Fuzzy inference ; Fuzzy neural networks ; Neural networks ; Nonlinear systems ; Soft computing ; Uncertainty analysis ; Existence conditions ; Linear-in-parameter models ; Uncertain nonlinear systems ; Uncertain parameters ; Z-number ; Nonlinear equations
  8. Source: 9th International Conference on Theory and Application of Soft Computing, Computing with Words and Perception, ICSCCW 2017, 22 August 2017 through 23 August 2017 ; Volume 120 , 2017 , Pages 923-930 ; 18770509 (ISSN)
  9. URL: https://www.sciencedirect.com/science/article/pii/S1877050917325395