AsynchronousMachineModel

a pll example in matlab

This file contain several functions for digital modulation simulation.

m序列产生子函数 gold序列产生子函数 自相关函数和互相关函数计算子函数 调用子函数[s,k,pn]=m(c,b0)产生第二条m序列(function [Rm]=Rm(golda,goldb) 自相关函数和互相关函数计算子函数 N=11 n=2^N-1 Rm(1)=sum(golda.*goldb)/n for k=1:n-1 Rm(k+1)=sum(golda.*circshift(goldb,[0,k]))/n end Rm )

fuzzy clustering for rule base

说明：  最小二乘法进行系统辨识，有学这方面的可以进行很好的参考(Least squares for system identification, a study in this area can be a good reference)

一种基于栅格法的移动机器人路径规划问题，可以自行改变障碍物位置坐标。(a robot path planning algorithm based on cell composition, you can change the position of obstacles. )

最新版改进Salam网络拓扑随机生成算法通用MATLAB源(The latest version to improve the network topology Salam GM MATLAB algorithm randomly generated source)

主要给出了用小波的二进制进行图像放大的一些程序(Are given for the use of wavelets for image to enlarge some of the binary program)

说明：  基于MATLAB的正交幅度调制系统仿真基于MATLAB的正交幅度调制系统仿真(MATLAB-based Simulation of quadrature amplitude modulation quadrature amplitude modulation MATLAB-based simulation)

lingjian.m-----蒙特卡罗方法 lingjian.m使用零件初始值，用蒙特卡罗方法算出总费用。其中使用了自己编制的正态分布随机数发生器产生正态分布随机数。lingjian.m是对蒙特卡罗方法的一次练习。 accyouhua为标定值的函数，而lingjian不是一个函数，在其中已给出了一组标定值的值。 退火确定标定值/unitanneal()----模拟退火 连续型多个变量组合优化问题 这是对模拟退火方法的一次练习，结果证明模拟退火确实是一个行之有效的方法。 当参数选择较好时（一般也伴随着运行时间的加长），模拟退火的结果较好，然而用MATLAB的FMIMCON()一般可达到更高的精度。(lingjian.m---- Monte Carlo method. 氀椀渀最樀椀愀渀.m Part initial value, using the Monte Carlo method to calculate the total cost. The preparation of their own normal distribution random number generator to generate normally distributed random numbers. lingjian.m is the first practice of the Monte Carlo method. accyouhua calibration value the function lingjian not a function, which gives the value of a set of calibration values. Annealing to determine the calibrated value/unitanneal ()---- Simulated Annealing 吀栀攀 continuous multiple variables combinatorial optimization problems 吀栀椀猀 is an exercise of the simulated annealing method, the results show that the simulated annealing is an effective method. 圀栀攀渀 the parameter selection is better (generally accompanied by a longer running time), simulated annealing results, however using MATLAB FMIMCON () generally achieve higher accuracy.)