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<p>A data-driven modeling problem of discrete fractional chaotic systems (DFCS) is studied in this paper. Firstly, a sparse identification framework is constructed and augmented by an iterative thresholding method. Then, by the matrix perturbation theory, the sparse matrix’s structure and vector field functions are determined together with the fractional order. A parameter estimation problem is obtained. Finally, the problem is solved by the famous interior point method. With small sample data, discrete fractional Lorenz system and Chua’s circuit models are provided to show the method’s efficiency.</p>

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Data-driven system inference for Caputo discrete fractional chaotic systems

  • 期刊论文

  • 发表日期：

    2025年6月20日

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  • Guang Yang,
  • Wei Zhu,
  • Guo-Cheng Wu,
  • Zhang Chen

摘要

A data-driven modeling problem of discrete fractional chaotic systems (DFCS) is studied in this paper. Firstly, a sparse identification framework is constructed and augmented by an iterative thresholding method. Then, by the matrix perturbation theory, the sparse matrix’s structure and vector field functions are determined together with the fractional order. A parameter estimation problem is obtained. Finally, the problem is solved by the famous interior point method. With small sample data, discrete fractional Lorenz system and Chua’s circuit models are provided to show the method’s efficiency.