计算机工程2011，Vol.37Issue(22)：162-163,167,3.
状态缺失多变量系统的极大似然辨识方法
Maximum-likelihood Identification Method for State-missing Multivariate System
摘要
Abstract
Maximum likelihood identification is proposed for parameter estimation of multivariable state-space models subject to missing states. The likelihood function conditioned on input, output and missing series is constructed. The influence of parameter estimation by missing mode is analyzed. And the modified Kalman filter suitable for state estimation with missing state is presented. And parameter estimation algorithm for maximization of likelihood function is given. Numerical simulation results show the effectiveness of the proposed method.关键词
系统辨识/极大似然辨识/多变量系统/数据缺失/卡尔曼滤波Key words
system identification/ maximum-likelihood identification/ multivariate system/ data-missing/ Kalman filtering分类
信息技术与安全科学引用本文复制引用
衷路生,樊晓平,杨辉,瞿志华,颜争,齐叶鹏..状态缺失多变量系统的极大似然辨识方法[J].计算机工程,2011,37(22):162-163,167,3.基金项目
国家自然科学基金资助项目(60870010,60864004,60904049) （60870010,60864004,60904049）
国家"863"计划基金资助项目(2008AA04Z129) （2008AA04Z129）
