Estimating parameters of Lorenz chaotic system with MCMC method

被引：0

作者：

Cao, Xiao-Qun ^{[1
]}

Song, Jun-Qiang ^{[1
]}

Zhang, Wei-Min ^{[1
]}

Cai, Qi-Fa ^{[2
]}

Zhang, Li-Lun ^{[1
]}

机构：

[1] College of Computer, National Univ. of Defense Technology, Changsha 410073, China

[2] 61741 Troops of PLA, Beijing 100071, China

来源：

Guofang Keji Daxue Xuebao/Journal of National University of Defense Technology | 2010年 / 32卷 / 02期

关键词：

Monte Carlo methods - Probability density function - Markov processes - Chaotic systems - Chains - Numerical methods - Probability distributions;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

Based on Bayesian theorem, a method is proposed to estimate the unknown parameters of Lorenz chaotic system using Markov Chain Monte Carlo (MCMC) method. Firstly, the posterior probability density function for unknown parameters is deduced. Secondly, taking the posterior probability as the invariant distribution, the Adaptive Metropolis algorithm is used to construct the Markov Chains. Thirdly, the converged samples are used to calculate the mathematic expectation of the unknown parameters. The results of numerical experiments show that the parameters estimated by the new method have high precision and the noise is filtered effectively from observations at the same time.

引用

页码：68 / 72

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