汽车振动自由度模型

实现得到一个简单函数的最优解 利于在其基础上进入深入 学习(To get a simple function to achieve the optimal solution to facilitate their access to in-depth study on the basis of)

小学数学一个奥数题目的编程，方便运算出此类型的题目(Primary Mathematics Olympiad title of a program to facilitate the operation of this type of subject)

这个是用MATLAB程序编写的回波消除的代码，效果不错，简单明了(This is a MATLAB programming with the echo cancellation code, the effect is good, simple and clear)

Conjugate gradient method

适合合成孔径雷达初学者的一些资料 用心收集的(Dedicated collecting some information for synthetic aperture radar beginners)

该算法一种结合改进的基于独立分量分析（ICA）提取算法和基于多层感知器和单向二叉决策树的多类支持向量机分类方法。(The algorithm is a combination of improved based on independent component analysis (ICA) algorithm and multi-class support vector machine classification method based on binary decision tree of the multilayer perceptron and unidirectional.)

matlab code for cepstrum

用多个正弦产生仿真信号,两个阵元接受到信号后,估计两信号的延迟(Simulation generated by a number of sinusoidal signals, the two array element received signal, it is estimated that the two signals delayed)

居于matlab的数字滤波器的设计,基于Matlab的简单语音处理系统设计(lvboqisheji)

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)