NakagamiFadeCoefficient

Genetic Algorithm for function maximization. Especially useful for functions with kinks and discontinuities, and where a good "starting point" is unavailable.

该代码实现了在鼠标框定区域内的Harris Corner的检测(This code deals with the detecting of harris corners with matlab)

公式法产生任意均值、方差和长度的高斯白噪声(Formula method produce any mean, variance and the length of the Gaussian white noise)

基于Matlab的神经网络工具箱应用使用情况简介(Based on Matlab neural network toolbox of the application of the use of brief)

遗传算法是模拟达尔文的遗传选择和自然淘汰的生物进化过程的计算模型。它的思想源于生物遗传学和适者生存的自然规律，是具有“生存＋检测”的迭代过程的搜索算法。遗传算法以一种群体中的所有个体为对象，并利用随机化技术指导对一个被编码的参数空间进行高效搜索。(Genetic algorithm is simulated Darwinian natural selection of genetic selection and evolution process model. It' s thought from biological genetics and survival of the fittest laws of nature, is a " survival+ detection" iterative process of search algorithm. Genetic algorithms in a group all the individuals targeted, and technical guidance on the use of a randomized coded efficiently search parameter space.)

本书循序渐进地介绍了matlab的详细函数命令，包括入门知识、数值计算功能、图像可视化处理功能、Notebook、程序设计、高级语言程序接口、图形用户界面以及动态仿真系统等高级功能。为使用户能融会贯通，本书最后给出了几个用matlab剞劂问题的实例。(This book describes step by step detailed matlab function command, including an introduction, numerical computing, image visualization capabilities, Notebook, program design, high-level language programming interface, graphical user interface and advanced features such as dynamic simulation system. To enable users to mastery, with book finally gives a few examples of the problem matlab Ji chisel.)

新版的求解L1范数最小化问题的凸优化工具包(new version for solve L1-norm minimization problem of convex optimization )

基于OFDM体制的雷达系统的matlab仿真，其中有详细的word说明和三个m文件。(OFDM-based radar system matlab simulation system, including a detailed description and three m word document.)

说明：  BP神经网络在MATLAB中的应用，有数据支撑(Application of BP neural network in MATLAB)

说明：  k-means 算法接受输入量 k ；然后将n个数据对象划分为 k个聚类以便使得所获得的聚类满足：同一聚类中的对象相似度较高；而不同聚类中的对象相似度较小。(The k-means algorithm accepts the input k; then divides n data objects into k clusters in order to make the obtained cluster satisfy: the similarity of objects in the same cluster is high; and the similarity of objects in different clusters Smaller.)