Fractal-fractional neuro-adaptive method for system identification

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 algori...

Full description

Saved in:
Bibliographic Details
Published inEngineering with computers Vol. 38; no. 4; pp. 3085 - 3108
Main Authors Zúñiga-Aguilar, C. J., Gómez-Aguilar, J. F., Romero-Ugalde, H. M., Jahanshahi, Hadi, Alsaadi, Fawaz E.
Format Journal Article
LanguageEnglish
Published London Springer London 01.08.2022
Springer Nature B.V
Subjects
Adaptation
Algorithms
CAE) and Design
Calculus
Calculus of Variations and Optimal Control; Optimization
Classical Mechanics
Computer Science
Computer-Aided Engineering (CAD
Control
Engineering
Fractal models
Fractals
Fractional calculus
Identification
Information technology
Math. Applications in Chemistry
Mathematical and Computational Engineering
Neural networks
Optimization
Original Article
System identification
Systems Theory
Nonlinear fractional differential equations
Variable-order fractional derivative
Fractional calculus
Artificial neural networks
Online AccessGet full text

Cover

More Information
Summary: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.
ISSN:0177-0667
1435-5663
DOI:10.1007/s00366-021-01314-w