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2022影响因子

(CJCR)

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## 留言板

 引用本文: 王呈, 陈晶, 荀径, 李开成. 基于混合滤波最大期望算法的高速列车建模. 自动化学报, 2019, 45(12): 2260−2267
Wang Cheng, Chen Jing, Xun Jing, Li Kai-Cheng. Hybrid filter based expectation maximization algorithm for high-speed train modeling. Acta Automatica Sinica, 2019, 45(12): 2260−2267 doi: 10.16383/j.aas.c190193
 Citation: Wang Cheng, Chen Jing, Xun Jing, Li Kai-Cheng. Hybrid filter based expectation maximization algorithm for high-speed train modeling. Acta Automatica Sinica, 2019, 45(12): 2260−2267

## Hybrid Filter Based Expectation Maximization Algorithm for High-speed Train Modeling

Funds: Supported by National Natural Science Foundation of China (61603156, 61973137), the Joint Fund for Basic Research of High Speed Railway (U1734210), and Beijing Jiaotong University Education Foundation (9907006519)
• 摘要: 针对高速列车非线性单质点模型的特殊结构及含有隐含变量问题, 提出一种基于混合滤波的最大期望辨识方法. 借助递阶辨识理论, 将高铁列车状态空间模型分解为线性子系统模型和非线性子系统模型. 进而, 分别利用卡尔曼滤波和粒子滤波对速度和位移状态进行联合估计. 最后, 使用最大期望方法辨识高铁列车子系统模型参数, 解决了隐含变量辨识问题. 和传统方法相比, 本文所提出方法计算量小, 且具有较高的辨识精度. 仿真对比实验结果验证了该方法的有效性.
• 图  1  参数误差$\tau$$k$变化曲线

Fig.  1  Parameter estimation error $\tau$ versus $k$

图  2  位移估计变化曲线($+$: 估计位移; $-$: 真实位移)

Fig.  2  Displacement estimation curve

图  3  速度估计变化曲线($+$: 估计速度; $-$: 真实速度)

Fig.  3  Velocity estimation curve

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##### 出版历程
• 收稿日期:  2019-03-19
• 录用日期:  2019-08-15
• 刊出日期:  2019-12-01

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