Description: 基于LMS算法的变换域的源程序，里面给出了学习曲线，迭代后的输出曲线-LMS algorithm based on the Domain source, which is a learning curve, the output iterative curve Platform: |
Size: 1024 |
Author:张健康 |
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Description: 研究利用RLS算法自适应均衡器纠正存在加性白噪声的信道的畸变。讨论特征值扩散度 对学习曲线的影响。 比较RLS算法和LMS算法在不同信噪比情况下的学习曲线。
-RLS algorithm using adaptive equalizer to correct the existence of additive white noise distortion channel. Eigenvalue discussion diffusivity impact on the learning curve. Comparison of RLS algorithm and LMS algorithm for different signal to noise ratio in the case of the learning curve. Platform: |
Size: 131072 |
Author:喻海 |
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Description: FIR 滤波器的自适应模拟，通过收敛性、学习曲线等方面对RLS 算法以及LMS 算法的不同特性进行了比较-FIR Filter simulation, through the convergence of the learning curve compared with the RLS algorithm, as well as the different characteristics of the LMS algorithm Platform: |
Size: 3072 |
Author:katherine |
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Description: 里面有DFE、LMS、RLS、NLMS的几种算法的学习曲线的比较，很适合学习和分析,The DFE, LMS, RLS, NLMS several algorithms learning curve comparison, it is suitable for study and analysis Platform: |
Size: 373760 |
Author:吴琳珠 |
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Description: 研究用于自适应均衡器的LMS算法。研究步长的影响。分别画出W=2.9时，mu= 0.01、0.04和0.08情况下的MSE学习曲线-Research for the adaptive LMS algorithm equalizer. Research on the impact of the step. Draw W = 2.9, respectively, when, mu = MSE learning curve 0.01,0.04 and 0.08 in case of Platform: |
Size: 1024 |
Author:susan |
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Description: 图1为均衡带限信号所引起失真的横向或格型自适应均衡器(其中横向FIR系统长M=11), 系统输入是取值为±1的随机序列，其均值为零；参考信号；信道具有脉冲响应：
式中用来控制信道的幅度失真(W = 2～4, 如取W = 2.9,3.1,3.3,3.5等)，且信道受到均值为零、方差(相当于信噪比为30dB)的高斯白噪声的干扰。试比较基于下列几种算法的自适应均衡器在不同信道失真、不同噪声干扰下的收敛情况(对应于每一种情况,在同一坐标下画出其学习曲线):
1）横向/格-梯型结构LMS算法
2）横向/格-梯型结构RLS算法
并分析其结果。-Figure 1 is a band-limited signal caused balanced horizontal or lattice distortion adaptive equalizer (FIR system in which the lateral length M = 11), the system input is the value of a random sequence of ± 1, mean zero reference signal channel has impulse response:
Wherein the amplitude of the distortion for the control channel (W = 2 ~ 4, and so as to take W = 2.9,3.1,3.3,3.5), and subjected to a channel with zero mean and variance (equivalent SNR is 30dB) Gaussian white noise. Compare the following types of algorithm-based adaptive equalizer in a different channel distortion, the convergence of different noise interference (corresponding to each situation, draw their learning curve in the same coordinate):
1) landscape/grid- LMS algorithm ladder structure
2) landscape/grid- RLS algorithm ladder structure
And analyze the results. Platform: |
Size: 3072 |
Author:李丽红 |
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Description: 学习曲线的LMS自适应算法，可以通过改变迭代次数观察效果-The learning curve of the LMS adaptive algorithm can be observed by changing the number of iterations Platform: |
Size: 2048 |
Author:chf |
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Search - LEARNING CURVE LMS