提出了一种自适应变步长恒模盲均衡算法，利用剩余误差信号的自相关函数估计值作为控制步长的因子来自适应改变步长的大小，克服了恒模算法存在的固有缺陷。
A new variable step-size CMA blind equalization algorithm is introduced to conquer the defects of CMA, in which the step size is controlled by the estimation of error signal's autocorrelation.
近些年来盲信道均衡引起了人们极大的兴趣，其中应用最广泛的自适应均衡算法是盲信道均衡恒模算法。
In the last few years, blind equalization techniques have gained an increasing interest. The most popular blind adaptation algorithm is the constant modulus algorithm (CMA).
尝试提出了变步长双模式恒模算法，并与神经网络结合应用于盲均衡研究中。
This paper presents variable step dual model constant modulus algorithm, which combine with neural network for blind equalization in nonlinear channel.
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