Universal approximation theorem for a radial basis function fuzzy neural network

被引：3
|
作者：
Shanthi, S. Anita [1 ]
Sathiyapriya, G. [1 ]
机构：
[1] AnnamalaiUniv, Dept Math, Annamalainagar 608002, India
来源：
MATERIALS TODAY-PROCEEDINGS | 2022年 / 51卷
关键词：
Radial basis function network; Output function; Gaussian membership function; Universal approximation;
D O I：
10.1016/j.matpr.2021.11.576
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
Radial basis function fuzzy neural network has one hidden layer. Inputs are used to find the mean and variance of the Gaussian activation function. In this article, the hidden layer weights which are taken as fuzzy numbers and the activation function values are used to determine the output function. The method of finding the output of this network is illustrated by an example. The output function of radial basis function fuzzy neural network act as a function approximator. Further, Universal approximation theorem for radial basis function fuzzy neural network is dealt with. This is proved using Stone Weierstrass Theorem. Copyright (C) 2022 Elsevier Ltd. All rights reserved.
引用
收藏
页码：2355 / 2358
页数：4
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