本文研究系统状态初值漂移和系统参数扰动对迭代学习控制算法收敛性的影响。
In this paper, the influence about system initial shift and system parameter disturbance on convergence of the algorithm is studied.
由核密度估计推导获得的高斯核均值漂移算法因收敛速度慢在应用中效率不高。
The Gaussian kernel mean-shift algorithm which is deduced from kernel density estimation has not been widely employed in applications because of its low convergence rate.
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