Solving Differential Equations with Z-Numbers Data by Gaussian Radial Basic Function

Developing a novel and practical method based on the radial-base grid network (RBF) as well as the third-order polynomial of the exponential function [Formula: see text] in order to solve the first-order differential equations based on [Formula: see text]-numbers is the aim of this paper. It is worth mentioning that the advantage of the proposed RBF is that sufficient information is not required. The RBF contains three distinct layers as follows: the input layer including the elementary nodes; the second layer including the hidden layers via high dimensions; and the output layer for responding and activating the patterns of the input layer. The obtained results revealed that this method could solve and approximate such problems under acceptable confidence.

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Publication Details

Journal
New Mathematics and Natural Computation
Published
2025-01-10
DOI
https://doi.org/10.1142/s1793005726500444
Primary Topic
Neural Networks and Applications
Type
article
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article

Solving Differential Equations with Z-Numbers Data by Gaussian Radial Basic Function

Mozhdeh Afshar Kermani, Leila Qalehe, Tofigh Allahviranloo
New Mathematics and Natural Computation
Neural Networks and Applications
article

Solving Differential Equations with Z-Numbers Data by Gaussian Radial Basic Function

Mozhdeh Afshar Kermani, Leila Qalehe, Tofigh Allahviranloo
article en

Abstract

Developing a novel and practical method based on the radial-base grid network (RBF) as well as the third-order polynomial of the exponential function [Formula: see text] in order to solve the first-order differential equations based on [Formula: see text]-numbers is the aim of this paper. It is worth mentioning that the advantage of the proposed RBF is that sufficient information is not required. The RBF contains three distinct layers as follows: the input layer including the elementary nodes; the second layer including the hidden layers via high dimensions; and the output layer for responding and activating the patterns of the input layer. The obtained results revealed that this method could solve and approximate such problems under acceptable confidence.

New Mathematics and Natural ComputationVol. 22(03)
Islamic Azad University North Tehran Branch (IR), Istinye University (TR)
Openalex Percentile: Top 100%
Neural Networks and Applications
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