Bounded Convergence of Forgetting Factor Least Square Algorithm for Time-Varying Systems
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).
Keywords：time-varying systemidentificationparameter estimationleast squaresbounded convergence
Publication Date：2002-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：5( 423-427 )