blindsignalseparate

说明：  关于WS小世界的仿真，对于初学者来说可能比较有用·~· 大家可以看看 是用matlab编写的(WS small world on the simulation, might be useful for beginners ~* we can see is written in matlab)

lscv in matlab, bandwith selection

MATLAB及在电子信息中的应用：q707有限长序列z变换，z逆变换。708是逆变换(MATLAB and the application of electronic information: q707 finite length sequence of z transform, z inverse transformation. 708 is the inverse transform)

performance study high data rates modulation techniques of W-CDMA in multipath fading channel

QAM实现程序 此程序实现了QAM信号的输出，通俗易懂。(QAM procedures to achieve the realization of this process the output of the QAM signal, easy to understand.)

full modelling of wind farm in pscad emtdc for every one

介绍了几种自适应数字波束形成的算法以及用MATLAB实现的例程(Introduced several adaptive digital beam forming algorithm and using MATLAB implementation of the routine)

说明：  谱估计的非参数DOA(Dorection of Arrive)方法----MUSIC(Multi Signal Classification，多重信号分类)算法(spectrum estimation of parameters DOA (Dorection of Arrive) method-M USIC (Multi Signal Classification, multiple signal classification) algorithm)

高速铁路路基沉降是一个复杂的系统过程,涉及不同时期、不同工况,选用单一的预测模型不足以反映路基整体的变形规律.Logistic模型及Verhulst模型的曲线均为S形,可以反映全过程的地基沉降与时间的关系.针对路基填筑期和降水期两个时段利用MATLAB软件编制程序计算模型参数,得到Logistic模型和灰色Verhulst模型,进而求得两者的最优加权组合模型.对比结果表明:在路基填筑期由两者的最优加权组合模型预测得到的结果与实测结果较吻合,拟合精度高于单一模型 而在降水期Verhulst模型的预测值更接近实测值,平均相对误差约为1.75％,拟合精度较高.(High-speed railway subgrade settlement process is a complex system involving different times and in different conditions, the choice of a single forecasting model does not adequately reflect the overall deformation of roadbed. Curve Logistic model and Verhulst models are S-shaped, the whole process can be reflected foundation settlement versus time. against roadbed construction period of two hours and precipitation using MATLAB software programming model parameters, the Logistic model and gray Verhulst model, and then get the optimal model of a weighted combination of the two. comparative results indicate : results and experimental results in the roadbed construction period by the weighted combination of the best of both models predicted than the anastomosis fitting accuracy than a single model precipitation predicted value of Verhulst model closer to the actual value of the average relative error approximately 1.75 fitting accuracy.)

neural network based fault finding relay