rosslerrun.m

图像受到裁剪攻击时的一些算法 只要输入图像和想要裁剪的位置 就可以得到想要的结果(Images by cutting attack some algorithm as long as the input image and want to cut position can be the desired results)

说明：  matlab实现运动模糊图像的模糊长度的自动鉴别(Matlab implementation of motion blurred image fuzzy length automatic identification)

利用mcra的方法对噪声功率谱估计达到语音增强的目的(estimate the spectrum of the noise in order to enhance the SNR of the speech)

改变AVI视频文件的分辨率，并自动生成改变大小后的avi文件(change the size of avi file, and automatically generate a avi file that have been resized.)

matlab实现机器生产排序问题最优解 (matlab machine production scheduling problem to achieve the optimal solution )

说明：  基于BP神经网络的锂离子电池剩余使用寿命预测(Residual service life prediction of lithium ion battery based on BPneural network)

介绍了几个基于EKF扩展卡尔曼滤波的例子，可以帮助更好的理解扩展卡尔曼滤波(Introduced a few based on extended Kalman filter EKF example, can help a better understanding of extended Kalman filter)

很好很实用的matlab工程函数包括：无优化实验等相关问题(Matlab works well very useful functions include: no optimization experiments and other related issues)

该代码为gps软件接收机的跟踪环路的matlab实现程序，可以实现简单的gps跟踪环路的功能。(The code for the gps tracking software receivers realize matlab program loop, you can realize a simple loop gps tracking functions.)

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)