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Master–slave model-based parallel chaos optimization algorithm for parameter identification problems

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Abstract

The parameter identification problem can be formalized as a multi-dimensional optimization problem, where an objective function is established minimizing the error between the estimated and measured data. In this article, a master–slave model (MSM)-based parallel chaos optimization algorithm (PCOA) (denoted as MSM-PCOA) is proposed for parameter identification problems. The MSM-PCOA is a novel global optimization algorithm, where twice carrier wave chaos search is employed as the master model, while the migration and crossover operation are used as the slave model. The MSM-PCOA is applied to the parameter identification of two different complex systems: bidirectional inductive power transfer system and chaotic systems. Simulation results, compared with other optimization algorithms, show that MSM-PCOA has better parameter identification performance.

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Acknowledgments

This work was supported in part by the National Natural Science Foundation of China (No. 61573133, No. 61203309) and Hunan Provincial Natural Science Foundation of China (No. 2015JJ3053).

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Authors and Affiliations

  1. College of Electrical and Information Engineering, Hunan University, Changsha, 410082, China

    Xiaofang Yuan, Ting Zhang & Xiangshan Dai

  2. College of Information and Electrical Engineering, Hunan University of Science and Technology, Xiangtan, 411201, China

    Lianghong Wu

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  1. Xiaofang Yuan
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  2. Ting Zhang
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  3. Xiangshan Dai
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Correspondence to Xiaofang Yuan.

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Yuan, X., Zhang, T., Dai, X. et al. Master–slave model-based parallel chaos optimization algorithm for parameter identification problems. Nonlinear Dyn 83, 1727–1741 (2016). https://doi.org/10.1007/s11071-015-2443-0

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