wlan_11n_simulink

a program about Traveling Salesman Problem (TSP) Genetic Algorithm

此程序描写了一个猜数过程，系统在给定范围内随机生成一个数，如何根据提示在较少的次数内才对这个数。(This procedure describes a guessing process, the system randomly generated within a given range of a number of tips on how to lower the number of times the number of fishes.)

matlab子函数，RPCA。已调试通畅，可直接调用，无需折腾。(matlab Functions, RPCA. Debugging has been smooth, can be called directly, without having to toss.)

说明：  应用于多峰值MPPT问题，采用PSO寻找最大功率点，具体实现在S-function中，仿真时建议找一个内存大的电脑用连续仿真，离散在这种仿真时容易出问题。自行读程序只需输入光伏电压电流即可实现，仿真多峰值问题时光伏板注意反并联二极管，输出为占空比，boost、隔离boost都可以使用(For multi-peak MPPT problem, PSO is used to find the maximum power point, which is realized in S-function. It is suggested that a computer with large memory be used for continuous simulation. Discrete simulation is prone to problems. The self-reading program can be realized only by inputting photovoltaic voltage and current. In the simulation of multi-peak problem, the photovoltaic board pays attention to the anti-parallel diode.)

合成孔径雷达sar信噪比相对于不同距离的MATLAB仿真程序，最后结果以图表形式给出(Synthetic aperture radar sar signal-to-noise ratio relative to different distances MATLAB simulation program, the final result is given in chart form)

matlab 演示最炫民族风。很给力 很好听(matlab demo and most unusual national air. Is to force a good listen)

数值分析的EULER法，大学数值分析算法，已检验，可以运行(THE EULER method numerical analysis, University of numerical analysis algorithms have been tested, you can run)

cma mma avma mcma 多种盲均衡算法 完全重构 归一化盲均衡 (cma mma avma mcma NPR )

量子遗传算法最大熵法图像多阈值分割程序下载。阈值的个数直接指定即可。(image segmentation based on quantum-inspired genetic algorithm)

位置指令为幅值为1.0的阶跃信号，r（k）=1.0。网络结构为1-4-1，高斯函数的参数值取Cj=[-2 -1 1 2]T ，B=[0.5 0.5 0.5 0.5]T 。 网络权值学习参数为η=0.30，α=0.05 。PID控制各参数的初RBF网络控制，被控对象为G（s）= 取采样时间为1ms，采用Z变换进行离散化，离散化后的被控对象为 y(k)=-den(2)*y（k-1）-den(3)*y(k-2)+num(2)*u(k-1)+num(3)*u(k-2) 始值为，kp=20， kd=0.3， ki=0.1。 (Position command for the step signal amplitude of 1.0, r (k) = 1.0. The network structure is 1-4-1, the Gaussian function parameters taken Cj = [-2-1 1 2] T, B = [0.5 0.5 0.5 0.5] T. Weight learning parameter η = 0.30, α = 0.05. PID control parameters at the beginning of each RBF network control, the controlled object is G (s) = take the sampling time is 1ms, using the Z transform discrete, discretized controlled object is y (k) =-den (2)* y (k-1)-den (3)* y (k-2)+num (2)* u (k-1)+num (3)* u (k-2) initial value, kp = 20, kd = 0.3, ki = 0.1.)