System Identification of Nonlinear State-Space Models with Linearly Dependent Unknown Parameters Based on Variational Bayes
System Identification of Nonlinear State-Space Models with Linearly Dependent Unknown Parameters Based on Variational Bayes
复制标题
基于变分贝叶斯的线性相关未知参数非线性状态空间模型的系统辨识
DOI：
10.9746/jcmsi.11.456
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
Yoshiharu Nishida
中科院分区：
文献类型：
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作者：
K. Fujimoto;A. Taniguchi;Yoshiharu Nishida
：在本文中，我们提出了一种基于变分贝叶斯的非线性状态空间模型参数估计方法。证明了隐藏状态的变分后验分布等价于增广非线性状态空间模型状态的后验分布。这使我们能够通过实现各种现有的过滤和平滑算法来估计隐藏状态的概率。使用该技术，提出了一种基于变分贝叶斯和非线性平滑器的非线性系统的系统辨识算法。它预计比现有结果更准确，因为它在执行变分贝叶斯推理时没有使用任何额外的近似。此外，数值例子证明了该方法的有效性。
: In this paper, we propose a parameter estimation method for nonlinear state-space models based on the variational Bayes. It is proved that the variational posterior distribution of the hidden states is equivalent to a posterior distribution of the states of an augmented nonlinear state-space model. This enables us to estimate the probability of the hidden states by implementing a variety of existing filtering and smoothing algorithms. Using this technique, a system identification algorithm for nonlinear systems based on variational Bayes and nonlinear smoothers is proposed. It is expected to be more accurate than the existing results since it does not employ any additional approximations in executing the variational Bayes inference. Furthermore, a numerical example demonstrates the e ff ectiveness of the proposed method.