The idea of affine projection and data-reusing in adaptive filtering field is applied to the modified constant modulus algorithm (MCMA).
该算法将自适应滤波领域中的仿射投影和数据重用思想引入修正常模算法(MCMA)盲均衡器。
In the MCMA, adjusting the scale coefficients of two gradient vectors can improve the convergence rate and reduce residual mean square error.
调整该算法中两个梯度矢量的比例系数，可提高该算法收敛速度、减少收敛后的均方误差。
In order to overcome the slow convergence rate of traditional CMA (Constant modulus algorithm), a Momentum algorithm based Constant modulus algorithm (MCMA) is proposed.
针对传统常数模算法收敛速度慢的缺点，提出了一种基于动量算法的常数模算法。
In the paper, a modified constant modulus algorithm (MCMA) is proposed. The proposed algorithm minimizes a modified error function and the learning-rate is multiplied by received sequences.
采用一种修正恒模算法(MCMA)，该算法使修正的误差函数最小并且自适应学习率由接收序列即时调整。
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。
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.
常数模算法是一种最为常用的盲均衡算法，普遍应用于恒包络信号和非恒包络信号的均衡，但存在收敛速度慢和剩余误差大的缺点。
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.
常数模算法是一种最为常用的盲均衡算法，普遍应用于恒包络信号和非恒包络信号的均衡，但存在收敛速度慢和剩余误差大的缺点。
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