DoubleFrequzeOneSource

ISODATA算法,对原本算法进行了简化,计算速度较快()

说明：  迫零均衡方法及结果，有图有说明，适合初学者使用。(Zero forcing equalization methods and results, there are pictures and instructions for beginners.)

matlab编写的水准网平差程序，由测量数据可计算水准网平差结果，得到不错的水准网数据(levelling network computation by use of surveying datas and TSL application)

实现聚类K均值算法： K均值算法：给定类的个数K，将n个对象分到K个类中去，使得类内对象之间的相似性最大，而类之间的相似性最小。 缺点：产生类的大小相差不会很大，对于脏数据很敏感。 改进的算法：k—medoids 方法。这儿选取一个对象叫做mediod来代替上面的中心 的作用，这样的一个medoid就标识了这个类。步骤： 1，任意选取K个对象作为medoids（O1,O2,…Oi…Ok）。 以下是循环的： 2，将余下的对象分到各个类中去（根据与medoid最相近的原则）； 3，对于每个类（Oi）中，顺序选取一个Or，计算用Or代替Oi后的消耗—E（Or）。选择E最小的那个Or来代替Oi。这样K个medoids就改变了，下面就再转到2。 4，这样循环直到K个medoids固定下来。 这种算法对于脏数据和异常数据不敏感，但计算量显然要比K均值要大，一般只适合小数据量。(achieving K-mean clustering algorithms : K-means algorithm : given the number of Class K, n will be assigned to target K to 000 category, making target category of the similarity between the largest category of the similarity between the smallest. Disadvantages : class size have no great difference for dirty data is very sensitive. Improved algorithms : k-medoids methods. Here a selection of objects called mediod to replace the center of the above, the logo on a medoid this category. Steps : 1, arbitrary selection of objects as K medoids (O1, O2, Ok ... ... Oi). Following is a cycle : 2, the remaining targets assigned to each category (in accordance with the closest medoid principle); 3, for each category (Oi), the order of selection of a Or, calculated Oi Or replace the consumption-E (Or))

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转载的求lyapunov指数的程序，希望对大家有所帮助。(lyapunov exponent )

this matlab source code is for starting

说明：  四阶龙格库塔法解内弹道方程，具有很好的精度，程序通俗易懂(Fourth-order Runge-Kutta method solution trajectory equation for interior_ballstic,)

递推最小二乘法 系统辨识 参数辨识 matlab仿真(Recursive least squares method)

Convert Text To Bin Convert Bin To Text image use to increase KB to upload. Not neccessary