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This scr ipt runs a simulation loop for either a linear or a DFE equalizer. It
uses the RLS algorithm to initially set the weights, then uses LMS thereafter to minimize execution time. It plots the equalized signal spectrum, then generates and plot
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回波抵消器中常用的几种自适应滤波算法有LMS, NLMS, RLS等算法。对现有主要算法的性能进行了分析，并对优缺点进行评价和比较。为了在收敛速度和运算量之间得到很好的折衷，对NLMS算法改进，得到了 PNLMS,Echo cancellation devices commonly used in adaptive filtering algorithm has several LMS, NLMS, RLS, such as algorithms. The main algorithm of t
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回波抵消器中常用的几种自适应滤波算法有LMS, NLMS, RLS等算法。对现有主要算法的性能进行了分析，并对优缺点进行评价和比较。为了在收敛速度和运算量之间得到很好的折衷，对NLMS算法改进，得到了 PNLMS-Echo cancellation devices commonly used in adaptive filtering algorithm has several LMS, NLMS, RLS, such as algorithms. The main algorithm of t
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利用MATLAB仿真软件对LMS和RLS两种算法进行仿真，通过仿真比较了两种算法的收敛速度，对两种算法收敛后的误码率进行分析，研究了步长对LMS算法均方误差性能曲线的影响和遗忘因子对RLS算法性能曲线的影响。-Using MATLAB simulation software for two types of LMS and RLS algorithm simulation, the simulation compares the convergence rate of two algorithm
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关于自适应滤波的算法仿真，其中应用了LMS算法和RLS算法，也比较了他们的性能-Adaptive filtering algorithms on the simulation, which applied the LMS algorithm and RLS algorithm, also compare the performance of their
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均衡技术是克服码间干扰(Inter-Symbol Interference,ISI)的有效措施,由于信道特性的随机性与时变性,实际中消除码间干扰最常用的是自适应均衡器。本文对基于最小均方(Least Mean Squares,LMS)算法和递推最小二乘(Recursive Least Squares,RLS)算法的自适应均衡器进行仿真研究,分析了信道特性与设计参数对自适应均衡器的收敛速度与稳态性能的影响。
-Equalization technique is to overcome inte
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拟使用基于LMS与RLS的自适应算法在MATLAB平台上对带有两个权的自适应线性组合器进行仿真,进而对两类算法的性能作比较，同时也考察了两种算法在不同参数条件下曲线收敛性的变化-Intending to use the LMS and RLS-based adaptive algorithm in the MATLAB platform with two pairs of the right to self-adaptive linear combiner is simulated, and t
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lms 算法的详细说明 （基于LMS与RLS算法的自适应均衡器性能研究）-lms detailed descr iption of the algorithm (LMS and RLS algorithm based adaptive equalizer performance of)
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Inter-symbol interference if not taken care off may
cause severe error at the receiver and the detection of signal becomes
difficult. An adaptive equalizer employing Recursive Least Squares
algorithm can be a good compensation for the ISI probl
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两种算法（LMS和RLS）实现CDMA系统盲多用户检测的仿真。结论验证了在加性高斯白招生信道下、同步DS-CDMA系统中接收机应用这两种盲多用户检测算法抑制多址干扰（MAI: multiple access interference）和码间串扰（ISI: inter symbol interference）的能力,仿真实验与理论推导相吻合。实验与理论都表明,RLS性能好于LMS，而LMS计算量明显小于RLS。-This paper discusses two algorithms (LMS an
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分别用LMS和RLS算法实现回音对消，并比较二者收敛性能，及不同信道参数对算法的影响-LMS and RLS algorithms were used to achieve echo cancellation, and compare the convergence performance of the two, and different channel parameters affect the algorithm
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盲信号分离(BSS)指在源信号混合和传输信道未知的情况下，只利用接收天线的输出观测混合信号抽取源信号的方法。本文简要阐述了常用的瞬时混合盲信号分离的LMS与RLS自适应算法，对RLS自适应算法重点研究分析了基于普通梯度与自然梯度的两种算法，并通过仿真实验来分析比较几种方法的性能。-Blind signal separation (BSS) refers to the source signal and transmission channel mixing unknown circumstanc
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This paper compares performance of nite impulse
response (FIR) adaptive linear equalizers based on the recursive least-squares (RLS) and least mean square(LMS) algorithms in nonstationary uncorrelated scattering wireless channels. Simulation resul
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关于LMS和RLS算法的详细的原理，性能比较以及程序，附程序说明-The principle of the LMS and RLS algorithms, performance comparison and procedures, with a descr iption of the procedures
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智能天线自适应算法的研究，比较了LMS、RLS和MVDR三种算法的性能。-Smart antenna adaptive algorithm study to compare the performance of the LMS, RLS and MVDR three algorithms.
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LMS和RLS算法的对比，及其性能分析，研究其性能曲线的变化-Comparison of LMS and RLS algorithm and its performance analysis to study changes in the performance curve
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Describes about performance comparison of RLS and LMS
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代码实现了二阶AR模型的最优权值递推，使用了LMS和RLS两种方法，对二者性能进行了比较，分别进行了单次和100次平均进行性能观察，并且仿真了不同步长因子对LMS算法的影响以及不同lamda值对RLS算法的影响。文档包含了模型的详细介绍以及2种方法的理论仿真和结果分析。代码以附在之后。-Code to achieve the optimum weights recursive second order AR model, the use of LMS and RLS are two method
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自适应滤波算法的实现，RLS与LMS收敛性能比较代码。(Comparison of RLS and LMS convergence performance code)
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计算机仿真研究基于 LMS 算法和 RLS 算法的自适应系统识别性能.通过已知结构的系统来模拟未知系统是系统识别/辨识的一种基本方法.(The performance of adaptive system identification based on LMS algorithm and RLS algorithm is studied by computer simulation. It is a basic method of system identification / recognit
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