mofang

说明：  基于MATLABD的线性二次型最优控制设计(MATLABD based on the linear quadratic optimal control design)

非常权威而且是非常新的matlab 7的学习资料,pdf的质量非常好,不是超星打印出来的!希望对大家学习matlab有帮助!(Very authoritative and very new matlab 7 of learning materials, pdf very good quality, not print out Chaoxing! Want to have to help everyone learn matlab!)

在matlab环境下，进行的遗传算法的介绍和相应的编程实例和解读。(Matlab environment, the introduction of the genetic algorithm and programming examples and interpretation.)

this paper is about the image watermarking using DWT+DCT+CDMA. it provides a good PSNR value then each technique seprately

GUI 电子日历 可以与世界时钟同步 并选择不同的日期 方便简洁(GUI electronic calendar and world clock synchronization and select a different date is convenient and simple)

Please send to you my code matlab forecast

说明：  蚂蚁所在的环境是一个虚拟的世界，其中有障碍物，有别的蚂蚁，还有信息素，信息素有两种，一种是找到食物的蚂蚁洒下的食物信息素，一种是找到窝的蚂蚁洒下的窝的信息素。每个蚂蚁都仅仅能感知它范围内的环境信息。环境以一定的速率让信息素消失。(The environment where ants are located is a virtual world, including obstacles, other ants, and pheromones. There are two kinds of information. One is food pheromone spilled by ants who find food, and the other is the pheromone of nest scattered by ants who find nest. Each ant can only perceive the environmental information in its scope. The environment makes pheromones disappear at a certain rate.)

L1最小范数约束压缩感知算法,提供一个程序参考, it is MATLAB code(L1 norm constraint compressive sesing,it is MATLAB code)

Implementation to linear, quadratic and logistic discriminant analysis, for examples

说明：  珍藏版，可实现，新K均值聚类算法，分为如下几个步骤： 一、初始化聚类中心 1、根据具体问题，凭经验从样本集中选出C个比较合适的样本作为初始聚类中心。 2、用前C个样本作为初始聚类中心。 3、将全部样本随机地分成C类，计算每类的样本均值，将样本均值作为初始聚类中心。 二、初始聚类 1、按就近原则将样本归入各聚类中心所代表的类中。 2、取一样本，将其归入与其最近的聚类中心的那一类中，重新计算样本均值，更新聚类中心。然后取下一样本，重复操作，直至所有样本归入相应类中。 三、判断聚类是否合理 采用误差平方和准则函数判断聚类是否合理，不合理则修改分类。循环进行判断、修改直至达到算法终止条件。(NewK-means clustering algorithm ，Divided into the following several steps: A, initialize clustering center 1, according to the specific problems, from samples with experience selected C a more appropriate focus the sample as the initial clustering center. 2, with former C a sample as the initial clustering center. 3, will all samples randomly divided into C, calculate the sample mean, each the sample mean as the initial clustering center. Second, initial clustering 1, according to the sample into the nearest principle clustering center represents the class. 2, as this, take the its recent as clustering center of that category, recount the sample mean, update clustering center. And then taking off, as this, repeated operation until all samples into the corresponding class. Three, judge clustering is reasonable Adopt error squares principles function cluster analysis.after clustering whether reasonable, no reasonable criterion revisio)