On-line Bayesian system identification
On-line Bayesian system identification
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在线贝叶斯系统识别
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
A. Chiuso
中科院分区：
文献类型：
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作者：
Diego Romeres;Giulia Prando;G. Pillonetto;A. Chiuso
我们考虑一个在线系统识别场景，其中新数据在给定时间可用。为了满足实时估计要求，我们提出了一种定制的贝叶斯系统识别程序，其中通过优化边际似然算法的一步更新来估计超参数。为此目的，考虑梯度方法和 EM 算法。我们将这种“一步”程序与标准程序进行比较，其中优化方法一直运行直到收敛到局部最小值。实验证实了该方法的有效性。
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.