PSO

说明：  基于协同优化算法的单目标轧制规程优化设计(Rolling schedule based on single-objective collaborative optimization optimized design)

基于cpso文章，给出的一个matlab代码，添加测试函数即可运行。(Based on cpso, given a matlab code, add test function can be run.)

说明：  这是一个信赖域方法的程序，简单实用，对初学者非常有用。(This is a trust region method procedures, simple, practical, very useful for beginners. )

说明：  实现三次样条差值，与matlab实现的精度一样(Realize the difference between cubic spline, and the precision to achieve the same matlab)

用matlab 7.0编写的滑模变结构仿真的例子。(S-function for continuous state equation function [sys,x0,str,ts]=s_function(t,x,u,flag) switch flag, Initialization case 0, [sys,x0,str,ts]=mdlInitializeSizes Outputs case 3, sys=mdlOutputs(t,x,u) Unhandled flags case {2, 4, 9 } sys = [] Unexpected flags otherwise error([ Unhandled flag = ,num2str(flag)]) end mdlInitializeSizes function [sys,x0,str,ts]=mdlInitializeSizes sizes = simsizes sizes.NumContStates = 0 sizes.NumDiscStates = 0 sizes.NumOutputs = 3 sizes.NumInputs = 1 sizes.DirFeedthrough = 1 sizes.NumSampleTimes = 0 sys=simsizes(sizes) x0=[] str=[] ts=[] function sys=mdlOutputs(t,x,u) x11d=sin(pi*t/2) x21d=cos(pi*t) x31d=1 sys(1)=x11d sys(2)=x21d sys(3)=x31d )

说明：  MATLAB实用教程（第2版）实例源代码程序EXE0704(MATLAB Practical Guide (2nd Edition) examples source code program EXE0704)

MATLAB控制系统仿真与设计实例，欢迎大家参考。(MATLAB simulation and control system design example, we welcome the reference.)

利用newmark方法计算无阻尼系统，并作图(newmark calculated without damping system)

各种LMS算法 包括变步长和归一化等，程序代码 参考！(bianbuchang LMS)

一共包含了5个ICA的算法，其中： fastica.m文件中的ICA算法是基于负熵的； m_fastica.m文件中的ICA算法是基于负熵的改进算法； fastica_kurt.m文件中的ICA算法是基于峭度的； fastica_ML.m文件中的ICA算法是基于互信息的； NLPCA.m文件中的ICA算法是基于非线性PCA的。(Contains a total of five ICA algorithm, in which: fastica.m file in the ICA algorithm is based on negative entropy m_fastica.m file in the ICA algorithm is based on negative entropy of the improved algorithm fastica_kurt.m file in the ICA algorithm is Based on Kurtosis fastica_ML.m file in the ICA algorithm is based on mutual information NLPCA.m file in the ICA algorithm is based on nonlinear PCA' s.)