A novel technique for solving fully fuzzy nonlinear systems based on neural networks

Raheleh Jafari*, Sina Razvarz, Alexander Gegov

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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Abstract

Predicting the solutions of complex systems is a crucial challenge. Complexity exists because of the uncertainty as well as nonlinearity. The nonlinearity in complex systems makes uncertainty irreducible in several cases. In this paper, two new approaches based on neural networks are proposed in order to find the estimated solutions of the fully fuzzy nonlinear system (FFNS). For obtaining the estimated solutions, a gradient descent algorithm is proposed in order to train the proposed networks. An example is proposed in order to show the efficiency of the considered approaches.
Original languageEnglish
Pages (from-to)93-107
Number of pages15
JournalVietnam Journal of Computer Science
Volume7
Issue number1
DOIs
Publication statusPublished - 18 Feb 2020

Keywords

  • estimated solutions
  • complex system
  • fully fuzzy nonlinear system
  • neural network

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