Parameter Identification of Fractional Order Chaotic System via Opposition Based Learning Bare-Bones Imperialist Competition Algorithm

Authors

Ting You^{1, *,} , Dongge Lei¹, Lulu Cai¹, Peijiang Li²

¹School of Electrical and Information Engineering, Quzhou University, No.78, Jiuhua North Road, Quzhou, Zhejiang, 324000, China

²Quzhou College of Technology, No.18, Jiangyuan Road, Quzhou, Zhejiang, 324000, China

^*Corresponding author. Email: youting@qzc.edu.cn

DOI

10.2991/ijcis.d.201223.001How to use a DOI?

Keywords

Parameter identification; Fractional order chaotic system; Imperialist competition algorithm; Opposition based learning

Abstract

In this paper, a new method is proposed to identify the parameters of fractional order chaotic system. The parameter identification is achieved by minimizing the mean square error between the states of original fractional chaotic system and those of the estimated one, in which the parameters to be identified are regarded as the optimization variables. To effectively solve the optimization problem, an improved meta-heuristic algorithm, i.e., opposition based learning (OBL) Bare-bones imperialist competition algorithm (OBL-BBICA), is proposed. The proposed OBL-BBICA introduces the OBL and Gaussian sampling into imperialist competition algorithm (ICA) to enhance the exploration ability of ICA, and thus, overcomes the drawbacks of premature phenomena of ICA. OBL-BBICA is adopted to search the optimal parameters of fractional order chaotic system. Experimental results show that the proposed method can accurately identify the parameters of fractional order chaotic system.

Copyright

© 2021 The Authors. Published by Atlantis Press B.V.

Open Access

This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Ting You
AU  - Dongge Lei
AU  - Lulu Cai
AU  - Peijiang Li
PY  - 2020
DA  - 2020/12/29
TI  - Parameter Identification of Fractional Order Chaotic System via Opposition Based Learning Bare-Bones Imperialist Competition Algorithm
JO  - International Journal of Computational Intelligence Systems
SP  - 453
EP  - 460
VL  - 14
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.d.201223.001
DO  - 10.2991/ijcis.d.201223.001
ID  - You2020
ER  -