matlab-concise-tutorial-examples

Kalman filter intuitive tutorial

特征提取提取了图像中各个标记区域的纹理特征及形状特征(Feature extraction to extract texture features and shape features of each labeled region in the image )

学习abaqus子程序umat的绝好例子，权威专家编写(Learning excellent examples of abaqus umat subroutine, leading experts to write)

用esprit算法估计复正弦加白噪声的信号频率，f给出正弦信号的频率估计值(Esprit algorithm with an estimated increase in complex white noise sinusoidal signal frequency, f the frequency of sinusoidal signal given the estimated value of)

Dynamic Modeling of DFIG and Wind Turbine-Generators The model provided is as simple as is appropriate for power system dynamic studies.

function [L,C] = kmeans(X,k) KMEANS Cluster multivariate data using the k-means++ algorithm. [L,C] = kmeans(X,k) produces a 1-by-size(X,2) vector L with one class label per column in X and a size(X,1)-by-k matrix C containing the centers corresponding to each class. Version: 07/08/11 Authors: Laurent Sorber (Laurent.Sorber@cs.kuleuven.be) References: [1] J. B. MacQueen, "Some Methods for Classification and Analysis of MultiVariate Observations", in Proc. of the fifth Berkeley Symposium on Mathematical Statistics and Probability, L. M. L. Cam and J. Neyman, eds., vol. 1, UC Press, 1967, pp. 281-297. [2] D. Arthur and S. Vassilvitskii, "k-means++: The Advantages of Careful Seeding", Technical Report 2006-13, Stanford InfoLab, 2006.

实现dft基本功能。对频率分辨率有特殊说明，希望对您有帮助。(The realization of the basic functions of dft. Frequency resolution of special note, I hope for your help.)

3GPP LTE-Advanced中OFDM实现仿真结果以及画出波形，为大家更加了解OFDM提供帮助(3GPP LTE-Advanced in OFDM achieve simulation results and draw the waveform, as we learn more about OFDM help Please help Google Translate improve quality for your language here.)

合成孔径雷达的RD算法程序合成孔径雷达的RD算法程序合成孔径雷达的RD算法程序(RD algorithm for synthetic aperture radar synthetic aperture radar, RD program algorithm of the RD process synthetic aperture radar algorithm program)

DHMM，离线隐马尔科夫模型用于模式分类的好例子，特征采用Kmeans进行离散化。(DHMM, off-line Hidden Markov Model good example for pattern classification, feature discretization using Kmeans.)