litekmeans

说明：  matlab开发环境,emd源代码程序，提供大家学习交流(matlab development environment, emd the source code program, to provide them to learn from the exchange)

平面桁架有限元计算程序，已知约束与在和条件，求解节点支反力(The plane truss finite element calculation program )

qpsk调制 信道仿真 采用蒙特卡洛 信号和噪声随机生成，莱斯信道(qpsk modulation and channel simulation using Monte Carlo signal and noise randomly generated, Rice channel)

自己编写的GABPM文件，用于基于数据的系统的辨识，辨识效果还不错。(This is a M file of GABPNN I have written for the identification based on data.The simulation result shows that the method is valid.)

simulation about DZ5 transformer in matlab

经典的三代相干体算法C1/C2/C3,用于地震资料解释中，进行不连续性监测。(compute coherency in seismic interpretation)

说明：  非常好用的程序，里面包括EMD分解和HHT谱分析的程序，可以很好地处理非线性数据(Very useful procedure, which include EMD and HHT spectrum decomposition process, can well handle nonlinear data)

利用matlab编程实现TrAdaBoost算法(Matlab for TrAdaBoost algorithm.)

基于总体最小二乘ESPRIT算法的DOA估计的程序代码(Based on total least squares algorithm ESPRIT DOA estimation procedure code)

统计模式识别工具箱（Statistical Pattern Recognition Toolbox）包含： 1，Analysis of linear discriminant function 2，Feature extraction: Linear Discriminant Analysis 3，Probability distribution estimation and clustering 4，Support Vector and other Kernel Machines( This section should give the reader a quick overview of the methods implemented in STPRtool. • Analysis of linear discriminant function: Perceptron algorithm and multiclass modification. Kozinec’s algorithm. Fisher Linear Discriminant. A collection of known algorithms solving the Generalized Anderson’s Task. • Feature extraction: Linear Discriminant Analysis. Principal Component Analysis (PCA). Kernel PCA. Greedy Kernel PCA. Generalized Discriminant Analysis. • Probability distribution estimation and clustering: Gaussian Mixture Models. Expectation-Maximization algorithm. Minimax probability estimation. K-means clustering. • Support Vector and other Kernel Machines: Sequential Minimal Optimizer (SMO). Matlab Optimization toolbox based algorithms. Interface to the SVMlight software. Decomposition approaches to train the Multi-class SVM classifiers. Multi-class BSVM formulation trained by Kozinec’s algorithm, Mitchell- Demyanov-Molozenov algorithm )