Solving First-Order Differential Equations of Z-Numbers’ Initial Value Using Radial Basic Function

IF 1.5 Q2 MATHEMATICS, APPLIED International Journal of Differential Equations Pub Date : 2020-05-26 DOI:10.1155/2020/5924847
Leila Qalehe, M. Afshar Kermani, T. Allahviranloo
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Abstract

In this paper, a method was proposed based on RBF for numerical solution of first-order differential equations with initial values that are valued by Z-numbers. The proposed method consists of two parts. The first part has stated the amount of limitation of the fragmentation solution, while the second part has described the assurance of the first part. The limitation section also has two parts. The first part has included the initial condition of the problem, while the second part has included the RBF network. The confidence interval was also considered as a function based on the probability function, which has calculated the confidence level of the first part (limitation). The RBF network or the radial-base grid network has three distinct layers: the input layer that is the set of elementary nodes (sensory units); the second layer is the hidden layers with high dimensions, in which the output layer that has responded to the network response and the activation patterns used in the input layer. The advantage of using RBF is that the use of this technique does not require sufficient information. It only relies on the domain and the boundary. In an example, we have showed that our proposed approach could approximate the problem with acceptable confidence.
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用径向基函数求解Z数初值的一阶微分方程
本文提出了一种基于径向基函数的一阶微分方程数值求解方法,该方程的初值为Z数。所提出的方法由两部分组成。第一部分阐述了碎片化解决方案的限制量,而第二部分描述了第一部分的保证。限制部分还有两个部分。第一部分包括问题的初始条件,第二部分包括RBF网络。置信区间也被认为是基于概率函数的函数,该函数计算了第一部分(限制)的置信水平。RBF网络或径向基网格网络有三个不同的层:输入层是基本节点(传感单元)的集合;第二层是高维的隐藏层,其中对网络响应做出响应的输出层和输入层中使用的激活模式。使用RBF的优点在于,使用该技术不需要足够的信息。它只依赖于域和边界。在一个例子中,我们已经表明,我们提出的方法可以以可接受的置信度近似该问题。
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CiteScore
3.10
自引率
0.00%
发文量
20
审稿时长
20 weeks
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