Fuzzy_Entropy

svkernel函数为核函数 ,容易实现 , 参照核函数的公式编写代码就成(matlab)

svd feature extraction

Bit-power loading algorithms based on Effective SINR Mapping techniques. Bit-power loading algorithms based on Effective SINR Mapping techniques. Bit-power loading algorithms based on Effective SINR Mapping techniques. (Bit-power loading algorithms based on Effective SINR Mapping techniques. Bit-power loading algorithms based on Effective SINR Mapping techniques. Bit-power loading algorithms based on Effective SINR Mapping techniques. Bit-power loading algorithms based on Effective SINR Mapping techniques. )

ABC算法的源码找来逐行做了中文注释，并以优化SVM参数为例，进行学习。(ABC algorithm source code got to do a progressive Chinese comments, and to optimize the SVM parameters, for example, to learn.)

通信中，解码，解卷积码，它为一个MATLAB编程(Binary random number generator to generate a random sequence program for MATLAB Programming)

数据的统计描述与分析的matlab实现与分析(Statistical description and analysis matlab realize and Analysis)

algorihtmo the ant metaheurist

基于matlab 2010rb搭建的buck-boost chopper直流变换器升降压斩波电路仿真模型，调试通过，适合本科生学习电力电子技术仿真。(2010 rb based on matlab to build the buck- boost chopper dc converter buck chopper circuit simulation model, debugging through, suitable for undergraduate study power electronic technology simulation.)

说明：  系统辨识中的各种辨识算法：如最小二乘法、递推最小二乘法、广义最小二乘、变遗忘因子递推算法等。(System Identification of identification algorithm : if the least-squares method, recursive least squares method, generalized least squares, changing the forgotten factor algorithm.)

说明：  在原始的fcm算法基础上，对算法中的聚类数c和加权指数m给出优选方法，进而而出了fcm参数优选自适应算法，通过人造数据与具有实际背景的数据验证可以看出该算法是有效的，该算法不但可以自适应的给出最佳的聚类数，而且可以验证聚类的有效性，达到最佳聚类的目的。(In the original fcm algorithm based on the number of clusters on the algorithm and the weighted index m given c preferred method, and then out of the fcm parameter optimization adaptive algorithm, artificial data and data validation with real background can be seen that algorithm is effective, the adaptive algorithm can give the best clustering number, and can verify the validity of clustering, to achieve the best clustering purposes.)