自组织状态空间模型参数初始分布搜索算法

甘敏; 彭辉; 黄云志; 董学平

doi:10.3724/SP.J.1004.2012.01538

自组织状态空间模型参数初始分布搜索算法

doi: 10.3724/SP.J.1004.2012.01538

1.
合肥工业大学电气与自动化工程学院合肥 230009;
2.
中南大学信息科学与工程学院长沙 410083;
3.
先进控制与智能自动化湖南省工程实验室长沙 410083

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- 文章访问数: 2003
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- 被引次数: 0
出版历程
- 收稿日期: 2011-01-04
- 修回日期: 2012-05-10
- 刊出日期: 2012-09-20

Initial Distribution Search Algorithm for Self-organizing State Space Model

1.
School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009;
2.
School of Information Science and Engineering, Central South University, Changsha 410083;
3.
Hunan Engineering Laboratory for Advanced Control and Intelligent Automation, Changsha 410083

摘要

摘要: 自组织状态空间模型为估计非线性非高斯状态空间模型中的未知参数提供了一种有效方法. 针对自组织状态空间模型中参数的初始分布难以确定的难点,提出了一种搜索自组织状态空间模型参数初始分布的算法. 所用搜索算法基于一种高效的进化模型,具有全局搜索能力,使得参数的初始分布向真实参数"移动". 数值实验分析结果验证了提出方法的有效性.
- 自组织状态空间模型 /
- 粒子滤波 /
- 参数估计 /
- 非线性 /
- 非高斯
Abstract: The self-organizing state space model provides an efficient approach to estimating unknown parameters in a nonlinear non-Gaussian state space model. However, a difficult problem is how to determinate the initial distributions of parameters for a self-organizing state space model. To address this problem, this paper proposes an algorithm to seek the initial distribution of parameters for a self-organizing state space model. The proposed algorithm is based on an efficient evolutionary computation model which has global search capability. It makes the initial distribution of parameters close to the true parameter situation. The results of numerical experiments show the effectiveness of the proposed algorithm.
- Self-organizing state space model /
- particle filter /
- parameter estimation /
- nonlinear /
- non-Gaussian

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参考文献(1)

[1]

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