计算机工程与应用2017，Vol.53Issue(6)：60-66,251,8.DOI:10.3778/j.issn.1002-8331.1509-0043
协方差重置的两阶段递推贝叶斯参数辨识算法
Two-stage recursive Bayesian parameter identification algorithm with covariance resetting
摘要
Abstract
In order to obtain unbiased estimates in the presence of colored noise, a two-stage recursive Bayesian identifica-tion algorithm is proposed based on auxiliary model principle and decomposing technique. In this algorithm, the original model is decomposed into two fictional sub-models firstly, and then identified respectively;the estimated noise variance and a new covariance resetting method are also integrated into the algorithm to obtain improved estimates. Compared with recursive least squares algorithm, the proposed algorithm can reduce the computational burden. According to the simu-lation, the estimation error of the proposed algorithms is smaller than that of the recursive least squares. An industrial appli-cation validates the proposed algorithm.关键词
两阶段递推算法/递推贝叶斯算法/最小二乘算法/协方差重置Key words
two-stage recursive algorithm/recursive Bayesian algorithm/the least squares algorithm/covariance resetting分类
信息技术与安全科学引用本文复制引用
景绍学..协方差重置的两阶段递推贝叶斯参数辨识算法[J].计算机工程与应用,2017,53(6):60-66,251,8.基金项目
国家自然科学基金(No.51477070) （No.51477070）
江苏大学研究生科研创新项目(No.KYXX_0003). （No.KYXX_0003）
