卡尔曼增益通过变遗忘因子进行改进，避免误差的累积。
Using improved Kalman filter to forecast and track. Kalman gain is improved through variable forgetting factor which will avoid the accumulation of errors.
新算法使用时变遗忘因子对误差进行指数加权平均来估计均方误差，并使用该因子改变自适应迭代过程中滤波器系数向量的更新方向。
In the new algorithm, a time-variant forgetting factor is introduced to estimate the Mean Square Er-ror (MSE) and change the updating direction of adaptive filter coefficient vector.
为了改善固定遗忘因子递推最小二乘(rls)算法在时变系统中的跟踪性能，提出一种改进的RLS算法。
In order to improve the tracking performance of the fixed forgetting factor RLS algorithm in the time-varying system, a modified RLS algorithm is proposed.
为了改善固定遗忘因子递推最小二乘(rls)算法在时变系统中的跟踪性能，提出一种改进的RLS算法。
In order to improve the tracking performance of the fixed forgetting factor RLS algorithm in the time-varying system, a modified RLS algorithm is proposed.
应用推荐