基于高斯混合模型聚类的非均匀采样非线性系统的多模型切换辨识
作者:
作者单位:

1.大连理工大学控制科学与工程学院;2.新疆大学电气工程学院

作者简介:

通讯作者:

中图分类号:

TP15

基金项目:

国家自然科学基金项目(面上项目,重点项目,重大项目)


Multi-model switching identification for non-uniformly sampled nonlinear systems based on Gaussian mixture model clustering
Author:
Affiliation:

1.School of Control Science and Control Engineering, Dalian University of Technology, Dalian, 116024, China;2.School of Electrical Engineering, Xinjiang University

Fund Project:

Natural Science Foundation of Xinjiang Uygur Autonomous Region

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    摘要:

    从概率统计方法出发,提出了一种基于高斯混合模型聚类与递推最小二乘算法的非均匀采样数据非线性系统的多模型建模方法。首先,采用高斯混合模型作为调度函数,使用极大似然估计算法(EM)迭代更新估计高斯混合模型中参数,从而通过每个子系统的高斯概率密度函数计算和比较来确定子系统的激活情况;其次,采用递推最小二乘算法估计局部子系统参数;第三,使用鞅收敛定理对提出的算法性能进行了分析;最后,通过非均匀采样系统的多模型建模实例证明本文提出方法的有效性。

    Abstract:

    Based on the probabilistic method, the multi-model modeling method of non-linear system with non-uniformly sampled data is proposed, which is based on Gaussian mixture model clustering and recursive least square algorithm. Firstly, the Gaussian mixture model is used as the scheduling function, and the maximum likelihood estimation algorithm (EM) is used to iteratively update and estimate the parameters of the Gaussian mixture model, so that the activation of each subsystem can be determined by calculating and comparing the Gaussian probability density function of each subsystem; secondly, the recursive least square algorithm is used to estimate the parameters of the local subsystem. Thirdly, the martingale convergence theorem is used to analyze the performance of the proposed algorithm; Finally, the effectiveness of the proposed method is proved by the example of multi-model modeling for non-uniformly sampled system.

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历史
  • 收稿日期:2020-05-31
  • 最后修改日期:2021-07-22
  • 录用日期:2020-11-05
  • 在线发布日期: 2020-12-01
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