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

Research output: Contribution to journal › Article › peer-review

Standard

A novel technique for solving fully fuzzy nonlinear systems based on neural networks. / Jafari, Raheleh ; Razvarz, Sina ; Gegov, Alexander.

In: Vietnam Journal of Computer Science, Vol. 7, No. 1, 18.02.2020, p. 93-107.

Research output: Contribution to journal › Article › peer-review

Harvard

Jafari, R, Razvarz, S & Gegov, A 2020, 'A novel technique for solving fully fuzzy nonlinear systems based on neural networks', Vietnam Journal of Computer Science, vol. 7, no. 1, pp. 93-107. https://doi.org/10.1142/S2196888820500050

APA

Jafari, R., Razvarz, S., & Gegov, A. (2020). A novel technique for solving fully fuzzy nonlinear systems based on neural networks. Vietnam Journal of Computer Science, 7(1), 93-107. https://doi.org/10.1142/S2196888820500050

Vancouver

Jafari R, Razvarz S, Gegov A. A novel technique for solving fully fuzzy nonlinear systems based on neural networks. Vietnam Journal of Computer Science. 2020 Feb 18;7(1):93-107. https://doi.org/10.1142/S2196888820500050

Author

Jafari, Raheleh ; Razvarz, Sina ; Gegov, Alexander. / A novel technique for solving fully fuzzy nonlinear systems based on neural networks. In: Vietnam Journal of Computer Science. 2020 ; Vol. 7, No. 1. pp. 93-107.

Bibtex

@article{55e7241041c9466590ac655018d0ba2e,
title = "A novel technique for solving fully fuzzy nonlinear systems based on neural networks",
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.",
keywords = "estimated solutions, complex system, fully fuzzy nonlinear system, neural network",
author = "Raheleh Jafari and Sina Razvarz and Alexander Gegov",
year = "2020",
month = feb,
day = "18",
doi = "10.1142/S2196888820500050",
language = "English",
volume = "7",
pages = "93--107",
journal = "Vietnam Journal of Computer Science",
issn = "2196-8888",
publisher = "World Scientific",
number = "1",

}

RIS

TY - JOUR

T1 - A novel technique for solving fully fuzzy nonlinear systems based on neural networks

AU - Jafari, Raheleh

AU - Razvarz, Sina

AU - Gegov, Alexander

PY - 2020/2/18

Y1 - 2020/2/18

N2 - 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.

AB - 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.

KW - estimated solutions

KW - complex system

KW - fully fuzzy nonlinear system

KW - neural network

U2 - 10.1142/S2196888820500050

DO - 10.1142/S2196888820500050

M3 - Article

VL - 7

SP - 93

EP - 107

JO - Vietnam Journal of Computer Science

JF - Vietnam Journal of Computer Science

SN - 2196-8888

IS - 1

ER -

ID: 18729814