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Description: 二乘法曲线拟合
//X,Y -- X,Y两轴的坐标
//M -- 结果变量组数
//N -- 采样数目
//A -- 结果参数
-Using two multiplication fit the curves//X,Y the site of two axial x,y//M the number of outcome variable//N the number of samples//A the parameter of outcome
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Size: 6511 |
Author: qiao |
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Description: 本程序的运行环境为matlab6.5，在command window下输入数据x和a，然后调用函数pca（x，a）（即本程序的主程序）输入的两个参数的意义是：一个是样本数据x，另外一个是主成分累积贡献率的一个闸值，作为选定主成分个数的一个重要数据。
即可得到样本的协方差矩阵，相关矩阵，相关矩阵的特征根及特征向量，主成分个数和主成分负荷矩阵。
-the procedures for the environment matlab6.5. under the command window in the input data and a x, then the function call pca (x, a) (that is the main program of the procedures) and input parameters of the two is the meaning of : a sample data is x, Another is a principal component of the cumulative contribution rate of a gate value, as a principal component selected a number of important data. Samples can be the covariance matrix, the correlation matrix, a correlation matrix eigenvalue and eigenvector. Principal component and the number of principal components load matrix.
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Size: 1466 |
Author: 郑可可 |
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Description: We address the problem of blind carrier frequency-offset (CFO) estimation in quadrature amplitude modulation,
phase-shift keying, and pulse amplitude modulation
communications systems.We study the performance of a standard
CFO estimate, which consists of first raising the received signal to
the Mth power, where M is an integer depending on the type and
size of the symbol constellation, and then applying the nonlinear
least squares (NLLS) estimation approach. At low signal-to noise
ratio (SNR), the NLLS method fails to provide an accurate CFO
estimate because of the presence of outliers. In this letter, we derive
an approximate closed-form expression for the outlier probability.
This enables us to predict the mean-square error (MSE) on CFO
estimation for all SNR values. For a given SNR, the new results
also give insight into the minimum number of samples required in
the CFO estimation procedure, in order to ensure that the MSE
on estimation is not significantly affected by the outliers.
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Size: 1265450 |
Author: 吴大亨 |
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Description: ML Estimation of frequency, phase, and amplitude of a sinusoid from discrete time samples
MLEsim.m
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Size: 1150 |
Author: 老邢 |
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Description: 报童问题的计算机仿真
％tm一轮实验的预定模拟天数
％t一轮实验的仿真天数累积值
％z订报量
％z 最优订报量
％g订报量z之上界
％s1损失值之累计值
％s最小损失值值
％r按概率分布产生随机售报量样本-newsboy problem of computer simulation% tm an experimental simulation of the target number of days a t% of the experimental days of accrued Simulation plot value volume%% z z Factory Workers optimal quantity% g subscription volume z% above the industry s1 loss value% cumulative value of the smallest losses's value % r value of the probability distribution of random reported sales volume samples
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Size: 1024 |
Author: 吴江华 |
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Description: 单输入单输出函数小波网络，用户需安装matlab神经网络工具箱，用户可以根据需要更换自己的训练样本，kugfcn.m,radbasmorlet.m分别为两个小波母函数，代码中加入了最优停止训练算法，非常好用-SISO function wavelet network, users need to install matlab neural network toolbox, users need to be replaced in accordance with their own training samples, kugfcn.m, radbasmorlet.m were two wavelet generating function, the code is added to optimal stopping training algorithm, very easy to use
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Size: 3072 |
Author: |
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Description: 使用c-均值聚类算法在IRIS数据上进行聚类分析，随机选择三个初始聚类中心，经过多次迭代，最终将150个样本分为三类。-Use c-means clustering algorithm in the IRIS data on the cluster analysis, three randomly chosen initial cluster centers, through a series of iterative, 150 samples will eventually fall into three categories.
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Size: 3072 |
Author: liz |
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Description: 用于手写体数字识别的USPS样本数据库和将MAT格式的样本数据库转换成二值化图像并以行程编码存储-For handwritten numeral recognition of the USPS database and a sample format of samples MAT database into binary image and the Run-Length Coding storage
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Size: 5558272 |
Author: 张凯兵 |
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Description: It is a signal processing project. It deals with the processing of electrocardiogram signals. The statement is in the enonce_presentation file. Several signal processing tools are used, as periodogram, correlogram. An algorithm to recover lost samples is implemented.
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Size: 20474880 |
Author: laetitia |
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Description: Script to show the fist 32 samples of y=cos(w0*n+fi)
for a fixed phase and a w0(angular frec) 0,pi/4,pi/2,3*pi/4
by actinio --vjgonzalezg@gmail.com--
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Size: 1024 |
Author: actinio |
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Description: Script to design an IIR Filter low pass using Butterworth & lineal transformation to convert
from analogic to digital a prototype filter.
by actinio vjgonzalezg@gmail.com :)
It plots:
1-Frecuency response of the filter impulse.
2-Magnitude response of the filter.
3-phase of the frecuency response.
4-Error of aproximation in bands.
It determines error of máximun aproximation on each band and the RMS value for the reject and pass band respectively
then, obtains plots of the first 50 samples for the following entries:
a)An rectangular pulse as the half of the longitud of the filter in n=0
b)A senoidal wave with amplitude =1 & frecuency 0.2 Fs.
c)A senoidal wave with amplitude = 1. & frecuency= 0.25 Fs.
d)A senoidal wave with amplitude = 1. & frecuency= 0.375 Fs.-Script to design an IIR Filter low pass using Butterworth & lineal transformation to convert
from analogic to digital a prototype filter.
by actinio vjgonzalezg@gmail.com :)
It plots:
1-Frecuency response of the filter impulse.
2-Magnitude response of the filter.
3-phase of the frecuency response.
4-Error of aproximation in bands.
It determines error of máximun aproximation on each band and the RMS value for the reject and pass band respectively
then, obtains plots of the first 50 samples for the following entries:
a)An rectangular pulse as the half of the longitud of the filter in n=0
b)A senoidal wave with amplitude =1 & frecuency 0.2 Fs.
c)A senoidal wave with amplitude = 1. & frecuency= 0.25 Fs.
d)A senoidal wave with amplitude = 1. & frecuency= 0.375 Fs.
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Size: 2048 |
Author: actinio |
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Description: 主元分析方法（PCA）是一种基于多元统计分析的数据降维方法, 它利用过程变量间的相关关系, 建立正常工况下的主元模型, 通过检验新的数据样本相对于主元模型的背离程度, 从而发现异常和故障。 -Principal Component Analysis (PCA) is based on multivariate statistical analysis of the data reduction method, which uses the correlation between process variables and establish the normal condition of the main element model, by testing samples of the new data relative to the principal component model departure from, and thus abnormal and failure.
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Size: 1024 |
Author: bonnie |
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Description: emoCode.m shows how to extract hierarchical multiscale LBP features.
GetMultiscaleLBP.m gets hierarchical multiscale LBP histogram for a given image
distMATChiSquare.m computes the dissimilarity between training samples and a test sample
GETMAPPING.m downloaded from http://www.ee.oulu.fi/mvg/
lbp_new.m downloaded from http://www.ee.oulu.fi/mvg/
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Size: 62464 |
Author: khadidja |
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Description: 此为模式识别中Parzen窗法估计概率密度函数。
全部程序流程如下：
1、读取FAMALE.TXT文件把身高或体重给数组，并求x1的样本数N1和窗宽、体宽；
2、读取MALE.TXT文件把身高或体重给数组，并求x2的样本数N2和窗宽、体宽；
3、读取Test2.txt文件把对应的身高或体重给数组A并求A的样本数M；
4、利用Parzen窗法估计概率密度函数判别男女性别；
5、对本判别的错误率进行统计。
-This is the pattern recognition Parzen window method to estimate the probability density function .
All program flow is as follows :
1 , read FAMALE.TXT file to height or weight to the array, and find the number of samples x1 N1 and window width , body width
2 , read MALE.TXT file to height or weight to the array, and find the number of samples x2 N2 and window width , body width
3 , read Test2.txt file to the corresponding height or weight requirements for the array A and A is the number of samples M
4 , the use of Parzen window method to estimate the probability density function of gender discrimination
5 , determine the error rate of the statistics .
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Size: 3072 |
Author: 崔杉 |
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Description: 此为模式识别中的Fisher线性判别方法求分类器。进行了男、女错误率和总错误率的统计。
全部程序流程如下：
1、读取FAMALE.TXT文件把身高或体重给数组，并求x1的样本均值m1和内离散度S1；
2、读取MALE.TXT文件把身高或体重给数组，并求x2的样本均值m2和内离散度S2；
3、求最佳变换向量和阀值点；
4、读取Test2.txt文件把对应的身高或体重给数组A并求A的样本数M；
5、把最佳变换向量和阀值点、x代入判别方程g（x）；
6、对本判别的错误率进行统计。-This is the pattern recognition method, to calculate the Fisher linear discriminant classifier .Were male , female and total error rate error rate statistics .
All program flow is as follows :
1 , read FAMALE.TXT file to height or weight to the array, and find the sample mean m1 x1 and internal dispersion S1
2 , read MALE.TXT file to height or weight to the array, and find the sample mean m2 and x2 within the dispersion S2
3 , for the best transformation vector and the threshold point
4 , read Test2.txt file to the corresponding height or weight requirements for the array A and A is the number of samples M
5 , the best transformation vector and the threshold point , x substituted into the discriminant equation g (x)
6 , determine the error rate of the statistics .
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Size: 3072 |
Author: 崔杉 |
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Description: this code describe channel model in matlab m file
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Size: 4096 |
Author: saman |
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Description: his package contains a set of functions for calling interfacing with
HTK from Matlab. Right now its mostly limited to training GMMs and
HMMs. It converts your Matlab data into a format that HTK understands
and calls HTK command line programs. The path to the HTK binaries is
hardcoded in get_htk_path.m-his package contains a set of functions for calling interfacing with
HTK from Matlab. Right now its mostly limited to training GMMs and
HMMs. It converts your Matlab data into a format that HTK understands
and calls HTK command line programs. The path to the HTK binaries is
hardcoded in get_htk_path.m
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Size: 2367488 |
Author: abdo |
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Description: Mann-Kendall检验不需要样本遵循一定的分布，也不受少数异常值的干扰，用来分析降水、径流、气温和水质等要素时间序列趋势变化(The Mann-Kendall test does not require samples to follow a certain distribution and is not disturbed by a small number of outliers. It can be used to analyze the trend of time series of elements such as precipitation, runoff, temperature and water quality)
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Size: 1024 |
Author: 等风吹
|
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Description: Delphi study on samples
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Size: 23109632 |
Author: ooodimitriusooo |
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Description: TO EXPLORE SYSTEM IDENTIFICATION USING RELEVANT EXAMPLES AND MANY MORE SAMPLES ARE UPLOADED.
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Size: 244736 |
Author: nithy |
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