Incrementing a global variable is 2 times slow than a local var.
递增一个全局变量要比递增一个局部变量慢2倍。
Let be a -mixing random variable sequence, and it is proved to be a theorem of complete convergence under the condition of slow mixing speed and non-identity distribution.
设为随机变量序列,文章在较弱的混合速度且非同分布的条件下证明了其完全收敛性的一个结果。
To solve the problem of slow convergence in the modified constant modulus algorithm (MCMA), a variable step and dual mode blind equalization algorithm is proposed, based on the MCMA algorithm.
为解决修正常系数模板算法(MCMA)收敛速度缓慢的问题,在MCMA算法的基础上,给出了一种变步长双模式MCMA算法。
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