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 parameter tracking error is given.

利用随机过程理论研究了遗忘因子最小二乘法(FFLS)的有界收敛性，给出了参数估计误差的上界。

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In this paper, based on -, the uniform convergence of the Kth order weak bounded variation functions on the sequence Spaces were investigated. Some equivalent conditions were also obtained.

本文在原有研究结果的基础上，讨论了叙列空间上的弱k级有界变差函数的一致收敛问题，得到了若干有关一致收敛的等价条件。

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We proved the closed systems global asymptotical stable, not needing the error upper bound and the error square integral, all the signals are bounded and the tracing error convergence.

在不要求最优逼近误差平方可积和上界已知的条件下，证明闭环系统全局渐近稳定，所有信号有界且跟踪误差收敛到零。

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