Fractal-fractional neuro-adaptive method for system identification

被引：27

作者：

Zuniga-Aguilar, C. J. ^{[1
]}

Gomez-Aguilar, J. F. ^{[2
]}

Romero-Ugalde, H. M. ^{[3
,4
]}

Jahanshahi, Hadi ^{[5
]}

Alsaadi, Fawaz E. ^{[6
]}

机构：

[1] Tecnol Nacl Mexico CENIDET, Interior Internado Palmira S-N, Cuernavaca 62490, Morelos, Mexico

[2] CONACyT, Tecnol Nacl Mexico CENIDET, Interior Internado Palmira S-N, Cuernavaca 62490, Morelos, Mexico

[3] Univ Grenoble Alpes, F-38000 Grenoble, France

[4] CEA, LETI, MINATEC Campus, F-38054 Grenoble, France

[5] Univ Manitoba, Dept Mech Engn, Winnipeg, MB R3T 5V6, Canada

[6] King Abdulaziz Univ, Fac Comp & Informat Technol, Dept Informat Technol, Jeddah, Saudi Arabia

来源：

ENGINEERING WITH COMPUTERS | 2022年 / 38卷 / 04期

关键词：

Fractional calculus; Nonlinear fractional differential equations; Variable-order fractional derivative; Artificial neural networks; DELAY-DIFFERENTIAL EQUATIONS; VARIABLE-ORDER; MODEL; NETWORKS; DESIGN; APPROXIMATION; DERIVATIVES; PREDICTION; ALGORITHM; OPERATORS;

D O I：

10.1007/s00366-021-01314-w

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

Neuronal networks are used in different fields of science and technology due to their capacity to approximate nonlinear functions through the synaptic weights optimization. This work shows a new form of optimization for neuronal networks based in fractional calculus. The fractional adaptation algorithm proposed was used to identify mechanical, electrical and biological systems. In each of the experiments a comparison between the proposed fractal-fractional model and the conventional model (with derivation order equal to one) was made.

引用

页码：3085 / 3108

页数：24

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