graph_theory

matlab code for nlms algorithm

this simulationto make table

可以对比较复杂的微分方程进行求解，比如说是二阶系数微分方程。(More complex differential equations can be solved, for example, is a second-order differential equation coefficients.)

This is the JPEG compression

reflectivity of dbr stack at different input angles

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))

说明：  matlab中编的基于遗传算法的转子故障特征选择的小程序，新手程度，仅供参考(matlab in the series based on genetic algorithm rotor fault feature selection of small programs, novice level, for reference only)

LDPC编码整个流程 ，编码，Sum-min译码 (LDPC encoding the entire process, coding, Sum-min decoding)

短时自相关算法的matlab程序 可以参考下(Short-term can refer to the autocorrelation algorithm matlab program)

说明：  山区地貌图 运用interp2进行二维插值，同时需要注意X、Y以及高度是否正确对应。(Mountain topographic map In interp2 interpolation, attention should be paid to the correct correspondence of X, Y and height.)