控制理论与应用2002，Vol.19Issue(3)：423-427,5.
时变系统遗忘因子最小二乘法的有界收敛性
Bounded Convergence of Forgetting Factor Least Square Algorithm for Time-Varying Systems
摘要
Abstract
Based on stochastic process theory, the bounded convergence of forgetting factor least square algorithm (FFLS for short) is studied and the upper bound of the paraneter tracking error is given. The analyses indicate that: i) for time-invariant deterministic systems, the estimates given by the FFLS algorithm converge to their true values at exponential rate; ii) for time-invariant stochastic systems, the FFLS algorithm can give a bounded mean square parameter estimation error; iii) for timevarying stochastic systems, the FFLS algorithm may track the time-varying parameters and its parameter tracking error is bounded (that is, the parameter tracking error is small when the parameter change rate is small).关键词
时变系统:辨识:参数估计/最小二乘/有界收敛性Key words
time-varying system/identification/parameter estimation/least squares/bounded convergence分类
信息技术与安全科学引用本文复制引用
丁锋,萧德云,丁韬..时变系统遗忘因子最小二乘法的有界收敛性[J].控制理论与应用,2002,19(3):423-427,5.基金项目
supported by National Natural Science Foundation of China (60074029, 69934010) and the Foundation of Information School of Tsinghua University (985-50-50). （60074029, 69934010）
