Fractional gradient descent algorithm for switching models using self-organizing maps: One set data or all the collected data

Jia Tang

Chaos, Solitons & Fractals, 2023, vol. 172, issue C

Abstract: This paper proposes two fractional gradient descent algorithms for switching models. Each submodel is assigned a weight which can determine the identity of the submodel in each sampling instant. By using the fractional gradient descent algorithms, the parameters of each submodel can be obtained, and then the weights of all the submodels can be estimated based on the self-organizing maps method. These two algorithms can deal with different kinds of switching models on a case by case basis. In addition, compared with the traditional identification algorithms, the proposed methods have two advantages: (1) has faster convergence rates; (2) has less computational efforts. Simulation example demonstrates the effectiveness of the proposed methods.

Keywords: System identification; Fractional gradient descent algorithm; Switching model; Convergence rate; Computational efforts (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:172:y:2023:i:c:s0960077923003612

DOI: 10.1016/j.chaos.2023.113460

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