Next Article in Journal
Statistical Properties of the Foreign Exchange Network at Different Time Scales: Evidence from Detrended Cross-Correlation Coefficient and Minimum Spanning Tree
Next Article in Special Issue
A Unification between Dynamical System Theory and Thermodynamics Involving an Energy, Mass, and Entropy State Space Formalism
Previous Article in Journal
A Novel Nonparametric Distance Estimator for Densities with Error Bounds
Previous Article in Special Issue
Outer Synchronization between Fractional-Order Complex Networks: A Non-Fragile Observer-based Control Scheme
Entropy 2013, 15(5), 1624-1642; doi:10.3390/e15051624

Genetic Algorithm-Based Identification of Fractional-Order Systems

, 1,*  and 2
Received: 14 March 2013; in revised form: 23 April 2013 / Accepted: 25 April 2013 / Published: 6 May 2013
(This article belongs to the Special Issue Dynamical Systems)
Download PDF [353 KB, uploaded 6 May 2013]
Abstract: Fractional calculus has become an increasingly popular tool for modeling the complex behaviors of physical systems from diverse domains. One of the key issues to apply fractional calculus to engineering problems is to achieve the parameter identification of fractional-order systems. A time-domain identification algorithm based on a genetic algorithm (GA) is proposed in this paper. The multi-variable parameter identification is converted into a parameter optimization by applying GA to the identification of fractional-order systems. To evaluate the identification accuracy and stability, the time-domain output error considering the condition variation is designed as the fitness function for parameter optimization. The identification process is established under various noise levels and excitation levels. The effects of external excitation and the noise level on the identification accuracy are analyzed in detail. The simulation results show that the proposed method could identify the parameters of both commensurate rate and non-commensurate rate fractional-order systems from the data with noise. It is also observed that excitation signal is an important factor influencing the identification accuracy of fractional-order systems.
Keywords: fractional-order systems; parameter identification; genetic algorithm; output error; noise; excitation fractional-order systems; parameter identification; genetic algorithm; output error; noise; excitation
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Export to BibTeX |

MDPI and ACS Style

Zhou, S.; Cao, J.; Chen, Y. Genetic Algorithm-Based Identification of Fractional-Order Systems. Entropy 2013, 15, 1624-1642.

AMA Style

Zhou S, Cao J, Chen Y. Genetic Algorithm-Based Identification of Fractional-Order Systems. Entropy. 2013; 15(5):1624-1642.

Chicago/Turabian Style

Zhou, Shengxi; Cao, Junyi; Chen, Yangquan. 2013. "Genetic Algorithm-Based Identification of Fractional-Order Systems." Entropy 15, no. 5: 1624-1642.

Entropy EISSN 1099-4300 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert