This article studies the parameter identification problem for bilinear state space models with time‐varying time delays. Considering the correlation of time delays, the Markov chain switching mechanism is adopted to model the delay sequence. Based on the observer canonical form, the bilinear state space model is transformed into a regressive form. A bilinear state observer is designed to estimate the state variables. Under the variational Bayesian scheme, the system parameters, discrete delays, and the Markov transition probabilities are identified by using the measurement data. A numerical example and a continuous stirred tank reactor simulation are employed to validate the effectiveness of the proposed algorithm.

中文翻译：

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具有马尔可夫切换时滞的双线性状态空间模型的变分贝叶斯辨识

本文研究具有时变时滞的双线性状态空间模型的参数辨识问题。考虑到时延的相关性，采用马尔可夫链切换机制对时延序列进行建模。基于观察者的规范形式，将双线性状态空间模型转换为回归形式。设计双线性状态观测器以估计状态变量。在变分贝叶斯方案下，通过使用测量数据来识别系统参数，离散延迟和马尔可夫转移概率。数值例子和连续搅拌釜反应器仿真被用来验证所提出算法的有效性。