suspension-with-gravity(chen-yi)

用matlabR2009b环境下，编写了300非常实用的程序，另附加对矩阵操作说明。。。(With matlabR2009b environment, the preparation of 300 very useful program. . .)

可以把mat格式的数据集转变为txt格式用来分类的文件。(You can format the data into a set of mat txt format used to classify documents.)

地层反射界面反射系数求取 ， 已知速度密度信息求取反射系数(Reflection layer interface reflection coefficient strike, strike a known speed-density information reflection coefficient)

SOGI及DS,QS传递函数MATLABm文件及伯德图绘制((SOGI, DS, QS Transfer Function MATLABm File and Bird Diagram Drawing)

采用遗传算法和三次样条插值法对已给数据进行拟合，得出最优取点方案(Genetic algorithm and cubic spline interpolation method is to fit the data, obtain optimum take-point program)

说明：  地球物理勘探过程中对地震记录数据的振幅进行求取(Geophysical data during the course of the amplitude of seismic records to strike)

说明：  本书的内容包括求解方程组、插值、微分方程及一系列。每章都有实例以帮助读者了解相关应用领域。并有一些matlab的代码，和题目的参考答案。(The content of this book includes solving equations, interpolation, differential equations, and a series. Each chapter has examples to help readers understand the relevant application areas. And there are some matlab codes, and reference answers to the questions.)

is esprit aoa algorithm

说明：  这是一本关于半导体物理学的书籍,从简单的基础入手,希望对相关研究方向的人员有所帮助.(This is a book on semiconductor physics, from the simple basics, hope the staff of the relevant research help.)

说明：  珍藏版，可实现，新K均值聚类算法，分为如下几个步骤： 一、初始化聚类中心 1、根据具体问题，凭经验从样本集中选出C个比较合适的样本作为初始聚类中心。 2、用前C个样本作为初始聚类中心。 3、将全部样本随机地分成C类，计算每类的样本均值，将样本均值作为初始聚类中心。 二、初始聚类 1、按就近原则将样本归入各聚类中心所代表的类中。 2、取一样本，将其归入与其最近的聚类中心的那一类中，重新计算样本均值，更新聚类中心。然后取下一样本，重复操作，直至所有样本归入相应类中。 三、判断聚类是否合理 采用误差平方和准则函数判断聚类是否合理，不合理则修改分类。循环进行判断、修改直至达到算法终止条件。(NewK-means clustering algorithm ，Divided into the following several steps: A, initialize clustering center 1, according to the specific problems, from samples with experience selected C a more appropriate focus the sample as the initial clustering center. 2, with former C a sample as the initial clustering center. 3, will all samples randomly divided into C, calculate the sample mean, each the sample mean as the initial clustering center. Second, initial clustering 1, according to the sample into the nearest principle clustering center represents the class. 2, as this, take the its recent as clustering center of that category, recount the sample mean, update clustering center. And then taking off, as this, repeated operation until all samples into the corresponding class. Three, judge clustering is reasonable Adopt error squares principles function cluster analysis.after clustering whether reasonable, no reasonable criterion revisio)