003小波变换及其MATLAB工具的应用

说明：  周克敏 鲁棒控制基础 （essential of robust control ） 部分习题(周克敏the basis of robust control (essential of robust control) part of Exercises)

MATLAB的一些实例，包括神经网络，小波变换，绘图，GUI等(Some examples of MATLAB, including neural networks, wavelet transform, graphics, GUI, etc.)

在瑞利衰落和高斯白噪声下qam调制对于不同信噪比的误码率(bit error rate of the Qam modulation in Rayleigh fading and Gaussian white noise for different signal to noise ratio)

获得一个二部图（两个部的顶点数要求一致）的最大匹配集，用于分配等问题的求解(get the maxinum_peipe )

MATLAB低通滤波，高斯滤波程序，共5个，对应分析的例子(MATLAB low-pass filter, Gaussian filter process)

说明：  多载荷多工况的结构拓扑优化代码，matlab实现(Structural Topology Optimization Code for Multi-load and Multi-working Conditions)

说明：  自适应中值滤波算法，可以用于图像去噪，比传统的中值滤波效果更好，运行时间更短(Adaptive median filter algorithm can be used for image denoising, than the traditional median filter is better, shorter running time)

帝王蝶优化算法,一种新的元启发式优化算法！(In nature, the eastern North American monarch population is known for its southward migration during the late summer/autumn the northern United States and southern Canada to Mexico, covering thousands of miles. By simplifying and idealizing the migration of monarch butterflies, a new kind of nature-inspired metaheuristic algorithm, called Monarch Butterfly Optimization (MBO), a first of its kind, is proposed in this paper. In MBO, all the monarch butterfly individuals are located in two distinct lands viz. Southern Canada & northern United States (Land 1) and Mexico (Land 2). Accordingly, the positions of the monarch butterflies are updated in two ways. Firstly, the offsprings are generated (position updating) by migration operator, which can be adjusted by the migration ratio. It is followed by tuning the positions for other butterflies by means of butterfly adjusting operator. In order to keep the population unchanged and minimize fitness uations, the sum of the newly-generated but)

字符识别、K-L人脸识别、虹膜识别、笔画识别等模式识别的例程和资料。(Character recognition, KL face recognition, iris recognition, stroke recognition, pattern recognition routines and information.)

基于遗传算法的道路图象分割,分割结果非常理想,可适用于各种环境.(The road based on genetic algorithms image segmentation, segmentation results are highly satisfactory and applicable to various environments.)