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- W4377102770 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." @default.
- W4377102770 created "2023-05-20" @default.
- W4377102770 creator A5030799367 @default.
- W4377102770 date "2023-07-01" @default.
- W4377102770 modified "2023-09-30" @default.
- W4377102770 title "Fractional gradient descent algorithm for switching models using self-organizing maps: One set data or all the collected data" @default.
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- W4377102770 doi "https://doi.org/10.1016/j.chaos.2023.113460" @default.
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