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The data files included are .MAT or *.dat (ASCII)files. The m-files and the data may be distributed, provided that the source is acknowledged in any publication and the data are not sold. Since this software is being distributed free of charge, the authors are not offering any technical support. Students who have any questions or difficulties using this software, or require the additional functions from the Signal Processing Toolbox should contact their professor. (The data files included are. MAT or*. dat (ASCII) files. The m-files and thedata may be distributed, provided that the source is acknowledged in anypublication and the data are not sold. Since this software is being distributedfree of charge, the authors are not offering any technical support. Students whohave any questions or difficulties using this software, or require theadditional functions from the Signal Processing Toolbox should contact theirprofessor.)

matlab建模与仿真 比较经典的一本书(Comparison of modeling and simulation matlab classic book)

在二维空间中变步长的LMS算法 (Adaptive filtering )

matlab语言编写，实现对字符进行分割。(Matlab language, the characters realize segmentation.)

关于Matlab的课件，里面含有数学建模的基础知识，对于数学建模的了解是很有帮助的(Courseware on Matlab, which contains the basics of mathematical modeling, mathematical modeling for understanding is helpful)

对二维图像利用莫尔条纹（云纹）进行加密，解密(The use of moire of two-dimensional image encryption, decryption)

包含了使用matlab对声音信号进行时域和频域分析，加高斯噪声，并用高通、低通、带通滤波器进行滤波，并显示波形图。(Contains the sound signal using matlab time domain and frequency domain analysis, plus Gaussian noise, and high-pass, low pass, band pass filter to filter and display waveforms.)

To find and estimate of the best solution and parameter in our problem we can use the evolutionary programming. PSO (particle swarm optimization) is an applicable method to catch this target. In these files I show the Simple PSO that it can be base of solve complicated problem, the code could run by MATLAB.

some lecture about the extend kalman filter algorithm ,which is very useful for the beginner.if you down load it ,it will be of great convenient for you.

The method works as follows. 1. For a data set with dimensionality, d, compute the variance of data in each dimension (column). 2. Find the column with maximum variance call it cvmax and sort it in any order. 3. Divide the data points of cvmax into K subsets, where K is the desired number of clusters. 4. Find the median of each subset. 5. Use the corresponding data points (vectors) for each median to initialize the cluster centers.