Specifically, the convergence rate of coefficient estimate is obtained in constant amplitude case.
特别地，对于常数振幅情形，得到了多项式系数估计的强收敛速度。
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。
Numerical results of typical problems show that it passes the constant curvature patch test and possesses stable convergence and high accuracy.
数值结果表明该单元能通过常曲率分片试验，收敛稳定并具有较好的精度。
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。
By introducing a parametric adaptation mechanism, the adaptive control system is able to achieve asymptotic tracking convergence in the presence of constant parametric uncertainties.
自适应控制通过引进参数自适应机制，在常参数不确定性存在的情况下，自适应控制系统能够实现跟踪误差渐近收敛于零。
This algorithm is based on constant module algorithm (CMA). The computer simulation results indicate that this algorithm have several advantages such as fast convergence, robustness and so on.
该算法基于恒模算法(CMA)，计算机仿真结果显示该算法收敛快速，性能稳定，能准确地完成多用户的分离。
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。
This article defines the sensitivity of dispersed variable. And we can use it to analyze the order of a infinite and judge the convergence of a constant term series.
给出离散变量的灵敏度之定义，且通过变量的灵敏度分析无穷大量的阶，从而判断常数项级数的敛散性。
For increasing the speed of convergence of the signed regressor constant modulus algorithm (SRCMA), a fast SRCMA algorithm suitable for real BPSK signals is presented.
为了加快符号回归常数模算法(SRCMA)的收敛速度，本文提出了一种适合于BPS K信号的快速算法。
The learning process use the constant learning rate in the traditional reinforce learning model, because of that robot learn in a low convergence speed and with the poor adaptation.
传统的强化学习模型在整个学习过程中使用恒定学习速率，导致在未知环境下收敛速度慢且适应性差。
The learning process use the constant learning rate in the traditional reinforce learning model, because of that robot learn in a low convergence speed and with the poor adaptation.
传统的强化学习模型在整个学习过程中使用恒定学习速率，导致在未知环境下收敛速度慢且适应性差。
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