论文标题
通过自由能量最小化，在振荡器中识别在线系统识别
Online system identification in a Duffing oscillator by free energy minimisation
论文作者
论文摘要
在线系统识别是针对输入和输出信号的每个测量的动态系统参数（例如质量或摩擦系数）的估计。在这里，悬挂振荡器的非线性随机微分方程被铸成生成模型，并使用在模型的因子图上传递变异消息来推断动态参数。通过对振荡器电子实现的数据进行实验，对该方法进行了验证。所提出的推理过程在最先进的非线性模型中执行以及离线预测误差最小化。
Online system identification is the estimation of parameters of a dynamical system, such as mass or friction coefficients, for each measurement of the input and output signals. Here, the nonlinear stochastic differential equation of a Duffing oscillator is cast to a generative model and dynamical parameters are inferred using variational message passing on a factor graph of the model. The approach is validated with an experiment on data from an electronic implementation of a Duffing oscillator. The proposed inference procedure performs as well as offline prediction error minimisation in a state-of-the-art nonlinear model.
