利用遥测系统实时监测水情资料，由于遥测系统自身的原因以及水文要素测量的具体要求，数据常常携带异常误差。采用有异常误差的实测流量资料对实时校正模型进行参数辨识，要求算法既能抵御异常误差的影响，又具有较强的实时跟踪能力，以适应实时洪水预报的要求。在递推最小二乘算法的基础上，引入抗差理论，削弱异常值对参数估计的影响；引入遗忘因子，实时跟踪模型时变参数的变化。计算实例表明，带有遗忘因子的抗差递推最小二乘算法对异常误差不敏感，又具有较强的实时跟踪能力。

Data observed by telemetric system often carries outliers resulting from instrument malfunctioning, false signal acquisition because of signal leak, collision and disturbance during signal transmission, and special measuring demand of hydrologic variable, in addition to unavoidable random errors. When the parameters of real-time co-correction model are estimated by the abnormal data, the algorithm must not only be able to resist the effect of the outliers, but also have ability for real-time tracing of the changes of parameters. In this paper, a robust recursive least-squares algorithm with a forgetting factor is produced based on the recursive least-squares algorithm. And a example is given to demonstrate that the algorithm is insensitive to the outliers and is adapt to the time-varying parameter estimation.