Based on the state space analysis, the time series analysis method for identification of the stochastic continuous signals, proved as consistent convergence, is given.
基于状态空间分析，给出了连续随机信号建模的时间序列分析方法，并证明了参数估计的一致收敛性。
In this paper, a method is presented for solving non differentiable equations in Banach space. At the same time, we analysis its convergence and get error estimates.
本文提出了一种解非线性不可微方程的迭代方法，分析了其收敛性并给出了误差估计，取得了很好的效果。
We prove the finite convergence of CLAR-LASSO and analyze its time and space complexity.
我们证明了CLAR- LASSO的有限收敛性，并分析了它的时间复杂度和空间复杂度。
But the blind estimation algorithm, for example, the sub-space decomposition, is not good for real time estimation because it requires large received signals, and has low estimation convergence rate.
但盲估计算法，如子空间分解法等，需要较大的样本值，收敛速率慢，不利于实时信道估计。
But the more iterative times, the more space-time consumption, execution speed and convergence speed are slower.
但其迭代次数多，时空消耗大，执行速度和收敛速度都还较慢。
But the more iterative times, the more space-time consumption, execution speed and convergence speed are slower.
但其迭代次数多，时空消耗大，执行速度和收敛速度都还较慢。
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