Flexible Least Squares Algorithm for Switching Models

Yunxia Ni, Lixing Lv, Yuejiang Ji and Qingling Wang

Complexity, 2022, vol. 2022, 1-11

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.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:hin:complx:2605570

DOI: 10.1155/2022/2605570

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