FUZZY FRACTIONAL MORE SIGMOID FUNCTION ACTIVATED NEURAL NETWORK APPROXIMATIONS REVISITED

被引：1

作者：

Anastassiou, George A. ^{[1
]}

机构：

[1] Univ Memphis, Dept Math Sci, Memphis, TN 38152 USA

来源：

MATHEMATICAL FOUNDATIONS OF COMPUTING | 2023年 / 6卷 / 03期

关键词：

Arctangent-algebraic-Gudermannian-generalized symmetrical activation functions; neural network fuzzy fractional approximation; fuzzy quasi-interpolation operator; fuzzy modulus of continuity; fuzzy derivative and fuzzy fractional derivative; VALUED FUNCTIONS; INTEGRALS; OPERATORS;

D O I：

10.3934/mfc.2022031

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Here we study the univariate fuzzy fractional quantitative approximation of fuzzy real valued functions on a compact interval by quasi-interpolation arctangent-algebraic-Gudermannian-generalized symmetrical activation function relied fuzzy neural network operators. These approximations are derived by establishing fuzzy Jackson type inequalities involving the fuzzy moduli of continuity of the right and left Caputo fuzzy fractional derivatives of the involved function. The approximations are fuzzy pointwise and fuzzy uniform. The related feed-forward fuzzy neural networks are with one hidden layer. We study also the fuzzy integer derivative and just fuzzy continuous cases. Our fuzzy fractional approximation result using higher order fuzzy differentiation converges better than in the fuzzy just continuous case.

引用

页码：320 / 353

页数：34

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