Radon-change

Fractal dimension using the box-counting method for 1D, 2D and 3D sets

详述了一些关于matlab中怎样应用模糊控制的材料，介绍了关于模糊控制的一些应用(Details about how the application of fuzzy control in matlab material presented on the correlation function of fuzzy control)

含噪语音的语音增强，包换多种算法的去噪效果对比，最后集中在一个操作界面上。(Speech enhancement in noisy speech, noise effect contrast to replacement of several algorithms, finally concentrated in one operating interface.)

it is for simple hill climbing algorithm

《雷达系统设计matlab仿真》书中的 matlab程序代码（10~13章） 【美】Bassem R.Mahafza 著 朱国富等译 电子工业出版社(" Matlab radar simulation system design," the book of matlab code (10 ~ 13) London and New York Bassem R. Mahafza with Guofu, M. Electronic Industry Press)

CIC补偿滤波器设计源代码，包含量化功能，可以作为FPGA开发滤波器设计数据。适用于CIC抽取和CIC插值滤波器的补偿滤波器应用。(CIC compensation filter design source code, including the quantization function can be used as a the FPGA development filter design data. Apply to CIC decimation filter compensation and CIC interpolation filter applications.)

说明：  MATLAB的使用说明书、参考手册，以及一些例程的说明和实现(The use of MATLAB instructions, reference manuals, as well as some routine specification and implementation)

数学建模经典问题轮船调配问题，详细解答了各个问题，并附有轮船到达的模拟程序(Mathematical modeling of the classic problems ship deployment issues, detailed answers to each question, along with the ship arrived simulation program)

matlab的pdf文档，主要讲解其中的控件技术，有兴趣的同志们可以参考一下(matlab the pdf documents, mainly on one of the control technology, the comrades who are interested can refer to)

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)