毕设时的压缩感知的各种经典还原算法，matlab源码

尽管matlab提供了朴素bayes的函数，但要理解朴素bayes还是自己编程实现为好。这是以IRIS数据集为例，用朴素bayes方法分类的程序。(Although matlab Bayes provides a simple function, but to understand the simple Bayes or programming for their own good. This is the IRIS data set as an example, naive Bayes method with a classification procedure.)

说明：  这是一部不错的MATLAB教程，拿出啦与大家分享了(This is a good tutorial of MATLAB。)

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)

风_柴_储能系统发电容量充裕度评估 关键词 储能 可靠性(The wind _ wood _ energy storage system generating capacity adequacy evaluation)

用matlab画的圆柱型光纤的LP01模的场分布图。所绘制的图像为3D图像，需要

Train a two layer neural network with a recursive prediction error

lingo软件的详细教程，适合初学者学习，看来便会用它解决复杂的规划问题。(lingo software detailed tutorial for beginners to learn, it would seem to solve complex planning problems.)

time variable problem using MATLAB

说明：  MATLAB下仿真水下目标分类，包括C原参数，能够实现两类水下目标的高效分类(Classification of simulated underwater targets in MATLAB)

信号分析与处理，第一部分_序列的产生和序列的傅立叶变换，第二部分_序列的卷积，第三部分_FT时域卷积定理验证，(Signal processing analysis and application.)