Initial Distribution Search Algorithm for Self-organizing State Space Model
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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.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.
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Key words:
- Self-organizing state space model /
- particle filter /
- parameter estimation /
- nonlinear /
- non-Gaussian
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