TDSWRX

说明：  模拟退火算法 自己写的一点小程序 提供一点参考(Simulated annealing algorithm to write their own applets provide a reference point)

说明：  模拟退火matlab源码，学习模拟退火很好的例子(Simulated annealing matlab source, learning good example of simulated annealing)

本人编写的各种matlab最优化程序代码，其中包含了FaNRXW问题最优化，无约束一维极值问题，牛顿法基本牛顿法，全局牛顿法，割线法，抛物线法，OgqdAfu法 Wolfe.Powell法，Powell法。 (I prepared a variety of optimization matlab code, which contains FaNRXW problem optimization, Unconstrained one dimensional extremum problem, Newton s method basic Newton method, global Newton method, secant method, parabolic law, OgqdAfu law Wolfe.Powell law , Powell method. )

VC_与MATLAB混合编程在语音处理中的应用(VC_ mixed with the MATLAB programming in Speech Processing)

协作频谱感知虽然可提高系统的感知性能， 但随着感知节点数目的增加， 系统资源的占用越来越多， 系统传输效率下降． 分析了协作频谱感知方法的感知性能， 得到协作频谱感知的感知性能与感知节点数目和接 收信噪比之间的关系， 提出了一种新的次级用户节点的选择方法， 该方法有效保证了所选择节点的感知性能， 仿真验证了该算法的有效性和可靠性．(Although the cooperative spectrum sensing can improve the perceived performance of the system, but with the perception of the number of nodes increases, more and more system resources, the system transmission efficiency decreased. Cooperative spectrum sensing method of analysis of the perceived performance of collaborative spectrum sensing by the perception of performance and perception of the number of nodes and the relationship between the received SNR, a new secondary user node selection method, which effectively guarantee perceived performance of the selected node, the simulation proves the validity and reliability.)

此为机器人模糊控制中的模糊控制规则，在模糊控制中加入此模糊控制规则，可得到仿真(This is the robot fuzzy control fuzzy control rules, fuzzy control fuzzy control rules by adding this can be obtained simulation)

MATKABB NICE VERY VERY

利用数据拟合概率密度函数 建立可靠度分析所需的概率分布函数(There is one outlier in the data of , but is not of data .For and the line of Q2 is in the middle of the box, so there are not asymmetries for both and .)

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.)

matlab GUI初步教程,介绍一些基本的操作及程序(course book)