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Flexible Least Squares Algorithm for Switching Models

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  • Yunxia Ni
  • Lixing Lv
  • Yuejiang Ji

Abstract

The self‐organizing model and expectation‐maximization method are two traditional identification methods for switching models. They interactively update the parameters and model identities based on offline algorithms. In this paper, we propose a flexible recursive least squares algorithm which constructs the cost function based on two kinds of errors: the neighboring two‐parameter estimation errors and the output estimation errors. Such an algorithm has several advantages over the two traditional identification algorithms: it (1) can estimate the parameters of all the sub‐models without prior knowledge of the model identities; (2) has less computational efforts; and (3) can update the parameters with newly arrived data. The convergence properties and simulation examples are provided to illustrate the efficiency of the algorithm.

Suggested Citation

  • Yunxia Ni & Lixing Lv & Yuejiang Ji, 2022. "Flexible Least Squares Algorithm for Switching Models," Complexity, John Wiley & Sons, vol. 2022(1).
    • Handle: RePEc:wly:complx:v:2022:y:2022:i:1:n:2605570
    DOI: 10.1155/2022/2605570
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    References listed on IDEAS

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    1. Kalaba, Robert E. & Tesfatsion, Leigh S., 1990. "Flexible Least Squares for Approximately Linear Systems," Staff General Research Papers Archive 11190, Iowa State University, Department of Economics.
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