FDTD

说明：  本程序是采用FDTD算法结合PML吸收边界，对对称三层平板光波导的模式场仿真(This program uses FDTD algorithm and PML absorbing boundary to simulate the mode field of symmetrical three-layer planar optical waveguide.)

采用四阶龙格库塔方法进行微分方程解算,用matlab编写的源代码,主要用于四元素微分方程的实时解算(Using fourth-order Runge-Kutta methods for differential equation solvers, prepared to use matlab source code, mainly for the four elements of real-time differential equation solver)

基于投影逼近的子空间跟踪（PAST）方法，用于自适应盲信号分离(Based on the projection approximation subspace tracking (PAST) method for adaptive blind signal separation)

加入重复控制，模拟数据分析处理的过程，通过虚拟阵元进行DOA估计。( Join repetitive control, Analog data analysis processing, Conducted through virtual array DOA estimation.)

用C语言程序通过先随机选取K个对象作为初始的聚类中心。然后计算每个对象与各个种子聚类中心之间的距离，把每个对象分配给距离它最近的聚类中心。聚类中心以及分配给它们的对象就代表一个聚类。一旦全部对象都被分配了，每个聚类的聚类中心会根据聚类中现有的对象被重新计算。(C Programming Language by first randomly selected the K object as initial cluster centers. And then calculate the distance between each object and each seed cluster center, and assign each object to its nearest cluster center. Cluster centers, and the object assigned to them on behalf of a clustering. Once all the objects are assigned to each cluster cluster centers will be based on the existing object clustering is recalculated.)

用matlab实现的改进的布谷鸟算法——SDCS，它是将最速下降法与CS相结合的(Matlab implementation of the improved algorithm of the cuckoo- SDCS , it will steepest descent method with CS )

3DFFT,对三维数据进行傅立叶变换，效果较好.可以用于三维的地震资料处理(3DFFT, three-dimensional Fourier transform of the data, the better can be used for three-dimensional seismic data processing)

回归分析，该支持向量机算法可用于预测，电力负荷、风力发电预测等(Regression analysis, the support vector machine algorithm can be used to forecast the power load, wind power prediction)

gm模型 gm模型 gm模型 gm模型 gm模型 GM(1,1)(gm model gm model gm model gm model gm model GM (1,1))

说明：  通过模型决定系数完成支持向量机参数优化，并建立基于大数据的支持向量机模型实现回归拟合(The parameters of SVM are optimized by the coefficient determined by the model, and the regression fitting is realized by the support vector machine model based on big data)