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Parameter identification for discrete memristive chaotic map using adaptive differential evolution algorithm

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Abstract

Since the concept of discrete memristor was proposed, more and more scholars began to study this topic. At present, most of works on the discrete memristor are devoted to the mathematical modeling and digital circuit implementation, but the research on its synchronization control has not received much attention. This paper focuses on the parameter identification for the discrete memristive chaotic map, and a modified intelligent optimization algorithm named adaptive differential evolution algorithm is proposed. To deal with the complex behaviors of hyperchaos and coexisting attractors of the considered discrete memristive chaotic maps, the identification objective function adopts two special parts: time sequences and return maps. Numerical simulations demonstrate that the proposed algorithm has the best performance among six existing algorithms, and it can still accurately identify the parameters of the original system under noise interference.

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Data availability statement

The datasets (MATLAB codes) generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

The authors would like to thank the anonymous reviewers for their constructive comments and insightful suggestions.

Funding

This work is supported by the National Natural Science Foundation of China (Grant Nos. 62071496, 62061008 and 61901530), the Natural Science Foundation of Hunan Province (No.2021JJ40545) and the Hunan Provincial Education Department (No. 20C1787).

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Authors and Affiliations

  1. School of Computer Science & School of Cyberspace Science, Xiangtan University, Xiangtan, 411105, People’s Republic of China

    Yuexi Peng

  2. School of Physics and Electronics, Central South University, Changsha, 410083, China

    Shaobo He & Kehui Sun

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  1. Yuexi Peng
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  2. Shaobo He
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Correspondence to Shaobo He.

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Peng, Y., He, S. & Sun, K. Parameter identification for discrete memristive chaotic map using adaptive differential evolution algorithm. Nonlinear Dyn 107, 1263–1275 (2022). https://doi.org/10.1007/s11071-021-06993-0

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