ARAPPara_matlab

4th generation long term evolution based on 3rd generation partnership program

GPS filtering down to prevent

说明：  PSO算法处理火焰模型，粒子群算法，作业用(PSO algorithm processing flame model)

visual c++ matlab图像处理与识别实用案例精选，帮助我们学习(visual c++ matlab image processing and recognition of practical case selected to help us learn)

两个遗传算法的实现程序，用MATLAB编写。实现效果良好。(This software is a implementation of genetic algorithm, which is written by MATLAB.Using this software, we can have a good result.)

LMI Introduction robust control(LMI Introduction)

本程序是基于分离谱算法的最小值选中次数加权算法的MATLAB源代码,可以对一维信号先进行谱分离,然后再在最小值的基础上对信号进行重建.(This procedure is based on the sub-out algorithm to select the minimum number of weighted algorithm MATLAB source code, can be conducted one-dimensional spectral signal separation, then the minimum value based on the signal reconstruction.)

说明：  一种改进的DTW算法，能有较好的处理效率(An improved DTW algorithm, can be a better deal with the efficiency of)

说明：  Matlab微分方程求解,一个利用Matlab进行微分方程求解的教程，简单实用。(Matlab for solving differential equations, one for the use of Matlab for solving differential equations of the tutorial, simple and practical.)

自举是一种由重采样估计，独立和（蒙特卡洛重采样）等概率设置一个单一的数据统计变化的一个途径。允许的措施估计那里的潜在分布是未知的或者样本量很小。他们的结果与这些分析方法的统计特性相一致。 在这里，我们使用非参数逼近。非参数引导更简单。它不使用该模型的结构，建造人工数据。矢量[易西]是重采样，而不是直接与replecement。这些参数是从这些对构建。 二，回归模型时，应使用在回归方程中的两个变量是随机的，会有错误的，即不是由研究者控制。模式，我用普通最小二乘回归低估了变量之间的错误时，他们都含有线性关系的斜率。据索卡尔和罗尔夫（1995），模型二回归的主题是一对哪些研究和争论仍在继续，最终建议是很难做出。 BOOTGMREGRESS模型II是一个引导程序。这需要s引导和规范样品前坡计算变量。这两个变量的每个转化为具有零均值和标准差的一个。由此产生的斜率是线性回归系数的Y在X里克创造了这个词（1973）几何平均数并给出了一个模型II回归广泛审查。它也被称为引导标准的主要轴线(The bootstrap is a way of estimating the variability of a statistic from a single data set by resampling it independently and with equal probabilities (Monte Carlo resampling). Allows the estimation of measures where the underlying distribution is unknown or where sample sizes are small. Their results are consistent with the statistical properties of those analytical methods. Here, we use the Non-parametric Bootstrap. Non-parametric bootstrap is simpler. It does not use the structure of the model to construct artificial data. The vector [yi, xi] is instead directly resampled with replecement. The parameters are constructed from these pairs. Model II regression should be used when the two variables in the regression equation are random and subject to error, i.e. not controlled by the researcher. Model I regression using ordinary least squares underestimates the slope of the linear relationship between the variables when they both contain error. According to Sokal and Rohlf (1995), t)