| 引用本文: | 杨小军, 潘 泉, 张洪才.基于Monte Carlo方法的自适应多模型诊断[J].控制理论与应用,2005,22(5):723~727.[点击复制] |
| YANG Xiao-jun,PAN Quan,ZHANG Hong-cai.Adaptive multi-model diagnosis using Monte Carlo method[J].Control Theory and Technology,2005,22(5):723~727.[点击复制] |
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| 基于Monte Carlo方法的自适应多模型诊断 |
| Adaptive multi-model diagnosis using Monte Carlo method |
| 摘要点击 2381 全文点击 1245 投稿时间：2004-05-08 修订日期：2004-10-18 |
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| DOI编号 10.7641/j.issn.1000-8152.2005.5.010 |
| 2005,22(5):723-727 |
| 中文关键词 多切换动态模型 混合估计 粒子滤波器 转移概率矩阵 自适应滤波 |
| 英文关键词 multiple switching dynamic models hybrid estimation particle filtering transition probability matrix adaptive filtering |
| 基金项目 国家自然科学基金资助项目(60172037,60404011,60372085) |
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| 中文摘要 |
| 多模型混合系统的模型切换服从有限状态的Markov链,其转移概率通常假定是已知的.当模型转移概率未知的时候,本文基于Monte Carlo粒子滤波器给出了混合系统状态估计的一种自适应算法.该算法假定未知的转移概率先验分布为Dirichlet分布,首先通过采样得到一组模型序列的随机样本,利用其中状态的转移次数计算先验转移概率,使用量测信息对样本更新选择后,获得模型转移概率的一种迭代的后验估计值,同时由粒子滤波器得到系统状态和模型概率的后验估计.将该方法用于混合系统的状态监测和故障诊断,仿真结果表明了算法的有效性. |
| 英文摘要 |
| Commonly,the models of hybrid system switch according to a finite state Markov chain with known transition probabilities.For state estimation of hybrid system with unknown transition probabilities,an adaptive estimation algorithm is proposed based on Monte Carlo particle filtering.The proposed algorithm assumes that the prior distribution of unknown transition probabilities follows Dirichlet distribution.First,a set of random samples of model sequence is achieved by sampling.Second,the prior transition probabilities are calculated by the frequency of model transitions in model sequence samples.Third,the posterior estimation of transition probabilities is achieved via measurement update and selection.Finally,the posterior estimation of state and model probability is obtained by particle filtering.In the state monitoring and multiple faults diagnosis of a class of hybrid system,the proposed method has been proved to be very effective. |
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