论文标题
在线性高斯系统中学习隐藏的马尔可夫模型，并应用于基于事件的状态估计
Learning Hidden Markov Models for Linear Gaussian Systems with Applications to Event-based State Estimation
论文作者
论文摘要
这项工作试图通过有限的隐藏马尔可夫模型（HMM）近似线性高斯系统，该系统可用于解决基于事件的复杂状态估计问题。开发了一种间接建模方法，其中首先为高斯系统确定了状态空间模型（SSM），然后将SSM用作学习HMM的模拟器。在提出的方法中，HMM的训练数据是从SSM通过构建量化映射生成的数据获得的。参数学习算法旨在通过利用HMM的定期结构特性来学习HMM的参数。分析了所提出算法的收敛性和渐近性。使用所提出的算法学习的HMM应用于事件触发的状态估计，模型学习和状态估计的数值结果证明了所提出的算法的有效性。
This work attempts to approximate a linear Gaussian system with a finite-state hidden Markov model (HMM), which is found useful in solving sophisticated event-based state estimation problems. An indirect modeling approach is developed, wherein a state space model (SSM) is firstly identified for a Gaussian system and the SSM is then used as an emulator for learning an HMM. In the proposed method, the training data for the HMM are obtained from the data generated by the SSM through building a quantization mapping. Parameter learning algorithms are designed to learn the parameters of the HMM, through exploiting the periodical structural characteristics of the HMM. The convergence and asymptotic properties of the proposed algorithms are analyzed. The HMM learned using the proposed algorithms is applied to event-triggered state estimation, and numerical results on model learning and state estimation demonstrate the validity of the proposed algorithms.
