On-line Bayesian System Identification

被引：0

作者：

Romeres, D. ^{[1
]}

Prando, G. ^{[1
]}

Pillonetto, G. ^{[1
]}

Chiuso, A. ^{[1
]}

机构：

[1] Univ Padua, Dept Informat Engn, Padua, Italy

来源：

2016 EUROPEAN CONTROL CONFERENCE (ECC) | 2016年

关键词：

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

We consider an on-line system identification scenario in which new data become available at given times. In order to meet real-time estimation requirements, we propose a tailored Bayesian system identification procedure in which the hyper-parameters are estimated through one-step-updates of an algorithm optimizing the Marginal Likelihood. To this purpose both gradient methods and an EM algorithm are considered. We compare this "1-step" procedure with the standard one, in which the optimization method is run until convergence to a local minimum. The experiments confirm the effectiveness of this approach.

引用

页码：1359 / 1364

页数：6

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