LongRange

Matlab图形的程序等练习，有需要的下吧吧用积分(Matlab graphics program such as exercise, there is a need to let it use the next integral)

用Matlab实现的规划问题，可以直接运行(To complete the guihua problem by matlab language)

说明：  利用MATLAB语言编写的直流电动机制动特性分析的程序(The use of MATLAB language of DC motor braking characteristics of process)

现代信号处理中常用的处理信号的滤波器，卡尔曼和维纳滤波器(Modern signal processing that are commonly used in signal processing, Kalman filters and Wiener filter )

word image create in matlab

说明：  模式识别matlab工具箱。一般常见的分类方法都包含在内，还有svm分类。(Pattern recognition matlab toolbox. General common classification methods are included, as well as svm classification.)

实现PSO对PID参数的优化，可以找到局部最优及全局最优参数。(PSO optimized PID parameters can be found in local optimal and global optimal parameters.)

改进的谱减法，语音增强，音频降噪-MATLAB(audio denoising---matlab)

电力系统潮流IEEE算例大全，有详细说明文档(Power flow IEEE examples Daquan, detailed documentation )

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)