Lungelo-DOL

用模糊神经网络逼近二维非线性函数，Matlab文件，附有说明文件。(Using fuzzy neural network approximation of two-dimensional nonlinear function, Matlab files, accompanied by documentation.)

Hide That Window!，可对指定窗口进行隐藏、修改透明度、修改标题、查看窗口类等(Hide That Window !, available for the specified window is hidden, change transparency, modify the title, the viewing window etc.)

自己编写子空间拟合进行DOA估计，包括信号子空间拟合，噪声子空间拟合。malab程序，效果很好！( )

abs制动系统仿真研究，对制动基础学习有一定作用(The simulation research of ABS braking system has a certain effect on the brake foundation.)

说明：  matlab,神经网络，基于RBF网络的动态设计(matlab, neural networks, RBF network based on dynamic design)

combplot many plots in only one figure

一个三相电机方波120逆变程序 占空比 未用到spwm(A Square Wave 120 Inverter Program for Three-phase Motor Duty cycle Unused SPWM)

2D FDTD Simulation program implemented in MATLAB

形态学，在模式识别系统中，判断两个形状的区别。(morphology )

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)