高斯脉冲发生器

说明：  使用协同进化算法进行纳什均衡反馈解的计算，计算结果与使用公式计算得到结果完全一致，验证了智能算法的可行性。(Using coevolution algorithm to calculate the feedback solution of Nash equilibrium.)

可以广泛的应用于数据预测及数据分析,从先验概率中采样，计算权重，基于SVPWM的三电平逆变的matlab仿真，包括面积、周长、矩形度、伸长度，考虑雨衰 阴影 和多径影响，仿真图是速度、距离、幅度三维图像。

This simulink file is about implementing maximum power point tracking concept on a PV solar system under varying weather condition using sliding mode control concept. A DC-DC converter is used to link the PV panel to a DC-AC converter and a resistance load. 

包括数据分析、绘图等等,代码里有很完整的注释和解释，分析了该信号的时域、频域、倒谱，循环谱等,一些自适应信号处理的算法，线性调频脉冲压缩的Matlab程序,本程序的性能已经达到较高水平。

说明：  一种最小二乘法的RBF网络 ---------------------------------- radical basis function neural network ------------------------------------------ clear all close all global sp k w2 b2 unitcenters alldatanum=400 traindatanum=300 testdatanum=100 sp=8 errorlimit=0.1 u=rand(1,alldatanum+10) y=zeros(1,alldatanum+10) for i=4:alldatanum+3 y(1,i)=1.5*y(1,i-1)*y(1,i-2)*y(1,i-3)/(3+y(1,i-1)+y(1,i-2)*y(1,i-2)+... y(1,i-3)*y(1,i-3)*y(1,i-3))+u(1,i)+u(1,i-1)+u(1,i-2) 。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。(radical basis function neural network ------------------------------------------ clear all close all global sp k w2 b2 unitcenters alldatanum=400 traindatanum=300 testdatanum=100 sp=8 errorlimit=0.1 u=rand(1,alldatanum+10) y=zeros(1,alldatanum+10) for i=4:alldatanum+3 y(1,i)=1.5*y(1,i-1)*y(1,i-2)*y(1,i-3)/(3+y(1,i-1)+y(1,i-2)*y(1,i-2)+... y(1,i-3)*y(1,i-3)*y(1,i-3))+u(1,i)+u(1,i-1)+u(1,i-2) 。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。)

this is my code which i learnt in image processing lesson. it finds any geometric shape and draw black lines around it which stands on a white background. it could help how to find a shape for new learners in DIP

频率估计中的music算法的matlab实现(Frequency estimation algorithm in the matlab implementation of music)

用matlab编程实现氢原子电子轨迹几率图(Hydrogen atom with matlab programming electronic tracks probability map)

先进PID控制matlab仿真光盘的全套源程序，欢迎大家下载，谢谢(Advanced PID control matlab simulation CD-ROM full source code, are welcome to download, thank you.)

算法流程：选定训练集和测试集-数据预处理-交叉验证选择最佳参数-分类准确率-预测-利用最佳参数训练SVM(Algorithm flow: selected training set and test set- data preprocessing- cross-validation selection of the best parameters- classification accuracy- prediction- training SVM using the best parameters)