Online identification of time-varying systems: A Bayesian approach
Online identification of time-varying systems: A Bayesian approach
复制标题
时变系统的在线识别：贝叶斯方法
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：
Giulia Prando;Diego Romeres;A. Chiuso
我们最近推出的正则化/贝叶斯系统识别程序的估计随时间变化的系统。具体来说，我们考虑在线设置，其中新数据在给定的时间步长变得可用。通过在边际似然最大化中仅通过一个梯度步骤估计超参数并通过利用脉冲响应估计的闭合形式可用性（当假定高斯先验和高斯测量噪声时）来满足由该设置施加的实时估计要求。通过使用遗忘因子，我们提出了两种方法来解决时变系统的跟踪问题。其中之一是将遗忘因子作为贝叶斯推理过程的超参数来估计。
We extend the recently introduced regularization/Bayesian System Identification procedures to the estimation of time-varying systems. Specifically, we consider an online setting, in which new data become available at given time steps. The real-time estimation requirements imposed by this setting are met by estimating the hyper-parameters through just one gradient step in the marginal likelihood maximization and by exploiting the closed-form availability of the impulse response estimate (when Gaussian prior and Gaussian measurement noise are postulated). By relying on the use of a forgetting factor, we propose two methods to tackle the tracking of time-varying systems. In one of them, the forgetting factor is estimated by treating it as a hyper-parameter of the Bayesian inference procedure.