论文标题
在线多项式NARMAX身份证的各种消息传递
Variational message passing for online polynomial NARMAX identification
论文作者
论文摘要
我们提出了一个在线非线性系统识别的变异贝叶斯推理程序。对于每个输出观察，都会更新一组参数后验分布，然后将其用于形成未来输出的后验预测分布。我们专注于多项式NARMAX模型的类别，我们将其施放为概率形式，并以福尼风格的因子图表示。该图中的推论通过传递算法的变分消息有效地执行。我们从经验上表明，我们的变异贝叶斯估计器的表现优于在线递归最小二乘估计器，最著名的是样本尺寸的设置和低噪声状态，并且与经过迭代的最小二乘估计器的离线培训相当。
We propose a variational Bayesian inference procedure for online nonlinear system identification. For each output observation, a set of parameter posterior distributions is updated, which is then used to form a posterior predictive distribution for future outputs. We focus on the class of polynomial NARMAX models, which we cast into probabilistic form and represent in terms of a Forney-style factor graph. Inference in this graph is efficiently performed by a variational message passing algorithm. We show empirically that our variational Bayesian estimator outperforms an online recursive least-squares estimator, most notably in small sample size settings and low noise regimes, and performs on par with an iterative least-squares estimator trained offline.
