用PSO方法搜索基于MLE的ARMA模型参数  

Searching ARMA Model Parameters MLE-Based by Applying PSO Algorithm

作　　者：罗航[1] 黄建国[1] 龙兵[1] 王厚军[1] 

机构地区：[1]电子科技大学自动化工程学院,成都610054

出　　处：《电子科技大学学报》2010年第1期65-68,共4页Journal of University of Electronic Science and Technology of China

基　　金：部级预研基金

摘　　要：针对小样本条件下用矩估计(ME)方法获取ARMA模型参数粗略的缺点,将粒子群优化算法(PSO)用于小样本ARMA模型参数的极大似然估计(MLE),以获得概率上最优的数字解。在分析基于ARMA模型似然函数的基础上,详细分析了PSO的思想、方法和评价指标。以实际例证显示了联合PSO优化方法估计AMAR模型参数的优良特性,并从算法和似然函数角度分别阐释了形成利弊的原因。On the condition of small samples, it is difficult to achieve ARMA model parameters by only using moment estimation （ME）. In this paper, a particle swarm optimization （PSO） algorithm is used to obtain optimal numeric solutions of small sample ARMA model in the sense of maximum likelihood estimation （MIE）. The principle and evaluation index of PSO are discussed in detail, which is based on analyzing likelihood function of ARMA model. Actual example shows that the joint PSO optimization method used for estimating AMAR model parameters has better characteristics in comparison with other methods. At the same time, some advantages and disadvantages are expatiated and analyzed from perspectives of algorithm and likelihood function.

关 键 词：ARMA模型 极大似然估计 粒子群 参数 小子样 

分 类 号：TP274[自动化与计算机技术—检测技术与自动化装置]