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MATLAB程序设计之线性算法和非线性源码比较MATLAB linear programming algorithm and nonlinear source comparison(MATLAB linear programming algorithm and nonlinear source more MATLAB linear programming algorithm and nonlinear source comparison)

里面包括了MATLAB的一些常用的函数的参考，对与学习的朋友们可以说是一个总结性的文档(Which includes a number of commonly used MATLAB function reference on friends and learning can be said to be a summary document)

最小均方误差（MMSE）的算法，包含位置式PID算法、积分分离式PID，是小学期课程设计的题目，仿真效率很高的，用于信号特征提取、信号消噪，大学数值分析算法，处理信号的时频分析，合成孔径雷达（SAR)目标成像仿真。( Minimum mean square error (MMSE) algorithm, It contains positional PID algorithm, integral separate PID, Is the topic of the elementary school stage curriculum design, High simulation efficiency, For feature extraction, signal de-noising, University of numerical analysis algorithms, When processing a signal frequency analysis, Synthetic Aperture Radar (SAR) imaging simulation target.)

基于Matlab的小波变换在图像压缩中的应用，这是学习资料，主要讲小波变换。(Matlab-based wavelet transform in image compression applications, this is the learning materials, the main speaker of wavelet transform.)

最大熵分割，成功调试过，可以完全实现最大熵分割，效果完美(Maximum Entropy Segmentation, successfully debugged Maximum Entropy Segmentation can be fully realized, the effect is perfect)

m-ary PAM simulation code

viterbi译码算法的matlab源代码(viterbi decoding algorithm matlab source code)

matlab classic program, which the code is simple, powerful, video material I have

Image Texture Classification Using Combined Grey Level Co-Occurrence Probabilities and Support Vector Machines Texture refers to properties that represent the surface or structure of an object and is defined as something consisting of mutually related elements. The main focus in this study is to do texture segmentation and classification for texture digital images. Grey level co-occurrence probabilities (GLCP) method is being used to extract features from texture image. Gaussian support vector machines (GSVM) have been proposed to do classification on the extracted features. A popular Brodatz texture album had been chosen to test out the result. In this study, a combined GLCP-GSVM shows an improvement over GLCP in terms of classification accuracy.

通过遗传算法计算下列函数的全局最优值 y(t)=sum(x(t,:).*x(t,:))(Global calculate the following functions by genetic algorithms optimal value y(t)=sum(x(t,:).*x(t,:)) )