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Numerical Solution of Linear Regression Based on Z-Numbers by Improved Neural Network

Somayeh Ezadia, Tofigh Allahviranloob

a Department of Applied Mathematics, Hamedan Branch, Islamic Azad University, Hamedan, Iran;
b Department of Mathematics, Science and Research Branch, Islamic Azad University, Tehran, Iran

* Corresponding Author: Tofigh Allahviranloo, email

Intelligent Automation & Soft Computing 2018, 24(1), 193-204. https://doi.org/10.1080/10798587.2017.1328812

Abstract

In this article, the researcher at first focuses on introducing a linear regression based on the Z-number. In this regression, observations are real, but the coefficients and results of observations are unknown and in the form of Z-rating. Therefore, to estimate this type of regression, we have three distinct ways depending on different conditions dominating the problem. The three methods are a combination of artificial neural networks and fuzzy generalized improvements of the technique. Moreover the method of calculating the weights of the Z-number neural network has been mentioned and the stability of neural network weights is considered. In some examples, the answer is estimated compared with the original answer.

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APA Style
Somayeh Ezadi, , Tofigh Allahviranloo, (2018). Numerical solution of linear regression based on z-numbers by improved neural network. Intelligent Automation & Soft Computing, 24(1), 193-204. https://doi.org/10.1080/10798587.2017.1328812
Vancouver Style
Somayeh Ezadi , Tofigh Allahviranloo . Numerical solution of linear regression based on z-numbers by improved neural network. Intell Automat Soft Comput . 2018;24(1):193-204 https://doi.org/10.1080/10798587.2017.1328812
IEEE Style
Somayeh Ezadi and Tofigh Allahviranloo, "Numerical Solution of Linear Regression Based on Z-Numbers by Improved Neural Network," Intell. Automat. Soft Comput. , vol. 24, no. 1, pp. 193-204. 2018. https://doi.org/10.1080/10798587.2017.1328812



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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