interface

此文件包含了在需求不确定的情况下给定客户服务水平或缺货率的EOQ经济订货批量模型的MATLAB源代码(This file contains the case of uncertainty in the demand for a given level of customer service or out of stock rate EOQ economic order quantity model for MATLAB source code)

MATLAB 学习指南第六章．编写与调用函数(MATLAB Learning Guide Chapter VI. Prepared with the call function)

这是我自己编写的一个改进的经验模态分解EEMD的源码，希望大家能用上(This is what I have written an improved empirical mode decomposition EEMD source code, I hope everyone can be on)

认知无线电中吞吐量和用户比较的仿真，可以在matlab下运行 (Throughput and user cognitive radio simulation can run in Matlab)

对多目标优化算法NSGA-II算法的改进，该算法进化代数少，但是获得的最终效果特别好！(pair of multi-objective optimization algorithm NSGA-II algorithm, the algorithm evolutionary less algebra, However, the ultimate effect was particularly good!)

读取matlab 文件夹中所有的文件路径 包括子文件夹的文件路径(Read all matlab folder including subfolders file path file path)

维纳滤波语音增强算法:语音增强算法的维纳滤波器，通过scalart 1996提出的实现。使用一个决策指导的方法跟踪的先验信噪比。请注意，该信号的第一0.25sec用于模型的噪声，即，它是假定的演讲之后，0.25sec开始(Wiener Filter for Noisy Speech Enhancement, implemented as proposed by Scalart 1996. Uses a decision directed method to track the a priori SNR. Note that the first 0.25sec of the signal is used to model the noise, that is, it is assumed that speech starts after that 0.25sec)

所有图论问题的MATLAB程序，包括最短路等等(MATLAB graph theory problem of all procedures, including the short-circuit, etc.)

基于matlab的多用户检测技术，有各种算法和论文。(Matlab-based multi-user detection technology, there are a variety of algorithms and papers.)

说明：  实现多尺度熵的算法 多尺度熵（Multiscale entropy, MSE）将样本熵扩展到多个时间尺度，以便在时间尺度不确定时提供额外的观察视角。样本熵的问题在于它没有很好地考虑到时间序列中可能存在的不同时间尺度。为了计算不同时间尺度下信号的复杂性，Costa等人（2002,2005）提出了多尺度熵。(MultiScale Entropy There has been considerable interest in quantifying the complexity of physiologic time series, such as heart rate. However, traditional algorithms indicate higher complexity for certain pathologic processes associated with random outputs than for healthy dynamics exhibiting long-range correlations. This paradox may be due to the fact that conventional algorithms fail to account for the multiple time scales inherent in healthy physiologic dynamics. We introduce a method to calculate multiscale entropy (MSE) for complex time series. We find that MSE robustly separates healthy and pathologic groups and consistently yields higher values for simulated long-range correlated noise compared to uncorrelated noise.)