5GreatMatlabBooks

Kmeans聚类算法以及改进的Kmeans聚类算法，包含收集的很多论文(Kmeans clustering algorithm and Kmeans clustering algorithm and improved, including a lot of paper collection)

鲁棒控制问题的线性矩阵不等式的解法，从理论到程序，自学很好用，希望对大家有帮助。(Robust control problem of linear matrix inequality solution, from theory to procedures, self-learning, very useful, and I hope to help everyone.)

用matlab实现的svm源程序。这个程序可以进行svm分类。(Svm matlab implementation with source code. This procedure can be svm classification.)

说明：  按FPE定阶的 源程序：fpe.cpp M序列：M序列.txt 白噪声：Gauss.txt 程序中先用依模型阶次递推算法估计模型的参数，再用fpe方法判断模型的阶次。 程序运行结果如下： n: 1 判断阶次FPE的值: 0.0096406 -0.481665 1.07868 n: 2 判断阶次FPE的值: 0.00875755 -0.446739 0.00498181 1.07791 0.0527289 n: 3 判断阶次FPE的值: 0.0087098 -0.459433 0.120972 -0.0569228 1.07814 0.0390757 0.116982 n: 4 判断阶次FPE的值: 0.000396884 -0.509677 0.4501 -0.200906 0.0656188 1.07991 -0.0156362 0.442989 0.0497236 n: 5 判断阶次FPE的值: 3.2095e-007 -1.18415 0.813123 -0.517862 0.34881 -0.116864 1.07999 -0.744141 0.474462 -0.253112 0.122771 n: 6 判断阶次FPE的值: 3.23349e-007 -1.14659 0.76933 -0.487651 0.329676 -0.10377 -0.00440907 1.07999 -0.703574 0.447253 -0.235282 0.113587 0.00479688 从以上结果可以看出，当n=5时，fpe值最小，所以这时的模型阶次和参数估计值为最优结果： 3.2095e-007 -1.18415 0.813123 -0.517862 0.34881 -0.116864 1.07999 -0.744141 0.474462 -0.253112 0.122771(err)

基于遗传算法的高校排课，是一段主程序，有点不大准确(GA )

合成孔径雷达成像程序，使用RD算法，对点目标进行仿真，实现点目标的成像(Synthetic aperture radar imaging procedures, the use of CS algorithm, the point target simulation to achieve point target imaging)

通信Matlab多径时变仿真实现，一个主程序附加几个子函数(Time-varying multi-path communication Matlab simulation, an additional several main Functions)

聚类分析（K-Means)程序实现及展现

The code demonstrate morphing that is basically source image warping followed by cross dissolving.

多机器人队形初始化，使用11个机器人最终编队形成一个圆形(Multi-robot formation initialization)