A Bayesian transfer sparse identification method for nonlinear ARX systems

被引:0
|
作者
Zhang, Kang [1 ]
Luan, Xiaoli [1 ]
Ding, Feng [1 ]
Liu, Fei [1 ]
机构
[1] Jiangnan Univ, Inst Automat, Key Lab Adv Proc Control Light Ind, Minist Educ, Wuxi 214122, Peoples R China
基金
中国国家自然科学基金;
关键词
Bayesian learning; nonlinear ARX system; sparse identification; transfer learning; STATE; REGRESSION; MODELS; ALGORITHMS; SELECTION; OUTPUT;
D O I
10.1002/acs.3884
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In this paper, we design a transfer sparse identification algorithm under the Bayesian framework through introducing other system knowledge into the system to be identified. This method provides a new identification solution for a nonlinear autoregressive model with exogenous inputs (NARX). The estimates of the transferred parameters are calculated by adding the transfer correction term to the un-transferred estimates. To achieve this, a joint prior distribution is devised for the parameters, ultimately enhancing the efficient utilization of existing data, reducing the reliance on new data, and achieving more accurate identification. The maximized marginal likelihood method is used to find the transfer gain and the transfer information matrix in the transfer correction term. Meanwhile, in order to make the algorithm automatically adapt to different data, we design an automatic structure detection method based on the transfer framework. The method automatically determines the sparsity threshold based on the maximum inter-class variance. Two examples are provided to demonstrate the advantages of our algorithm.
引用
收藏
页码:3484 / 3502
页数:19
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