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An efficient parameter estimation method for nonlinear high-order systems via surrogate modeling and cuckoo search

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Abstract

This work developed an efficient parameter estimation method for nonlinear high-order systems using surrogate modeling and cuckoo search. Specifically, to address the heavy computational burden required for evaluating the candidate parameters, we utilized a low-dimensional surrogate model to approximate the original system. The surrogate model was constructed by employing the proper orthogonal decomposition and the discrete empirical interpolation method. Then, to obtain the parameters of the original system, we applied the cuckoo search algorithm to solve the optimization problem that was built on the surrogate model. The accuracy and efficiency of the proposed method were verified on two numerical experiments, dealing with the identification of parameters for the FitzHugh–Nagumo system and the predator–prey system. The results showed that our approach yields accurate results while significantly reducing the computational cost.

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Acknowledgements

The authors gratefully appreciate the support by National Natural Science Foundation of China (Nos. 11871400 and 11971386) and the Natural Science Foundation of Shaanxi Provincial (Grant No. 2017JM1019). We thank the anonymous reviewers for their valuable comments and suggestions.

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Authors and Affiliations

  1. Research Center for Computational Science, School of Science, Northwestern Polytechnical University, Xi’an, 710129, China

    Xuefang Lai, Xiaolong Wang & Yufeng Nie

  2. School of Science, Xi’an Polytechnic University, Xi’an, 710048, China

    Xingshi He

Authors
  1. Xuefang Lai
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  2. Xiaolong Wang
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  3. Yufeng Nie
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  4. Xingshi He
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Correspondence to Yufeng Nie.

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Lai, X., Wang, X., Nie, Y. et al. An efficient parameter estimation method for nonlinear high-order systems via surrogate modeling and cuckoo search. Soft Comput 24, 17065–17079 (2020). https://doi.org/10.1007/s00500-020-04997-3

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