reverb

本文档是本人的自适应控制大作业，作业里分模型参考自适应控制（用smulink仿真）和CARMA模型的最小方差自适应控制，文中有详细的smulink框图和matlab程序，格式美观，分析透彻，图形整齐，对学习自适应控制的朋友有很大的帮助，谨以本人微薄的力量和成果与大家共鸣，(This document is my adaptive control large operations, operating in sub-model reference adaptive control (with smulink simulation) and the CARMA model of minimum variance adaptive control, the text has a detailed diagram smulink and matlab procedures, aesthetic format, analysis and thorough , graphics, neat, in learning adaptive control of a friend has a great help, I would like my meager results of the strength and resonance with the U.S.,)

matlab tutor for chinese. and in chinese.so who need it I think should be Chinese.

matlab 例程，粒子群优化算法，很全，希望对大家有所帮助(Matlab routines, particle swarm optimization algorithm, is very wide, and they hope to help everyone)

the CPML ABC in the two-dimensional TEz FDTD grid.

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 )

Malla de PAT proyecto

基于细菌觅食算法的优化计算的matlab程序实现(Bacteria foraging optimization algorithm based on the calculation of the matlab program implements)

matlab用于处理图像图像的应用，全书所有的代码都在(matlab image processing images for applications, all of the code in the book)

les algorithmes chekout et dv-hop en matlab

通过遗传算法计算下列函数的全局最优值 y(t)=(x2(t)-5.1/(4*pi*pi).*x1(t).^2+5/pi.*x1(t)-6).^2+10.*(1-1/(8*pi)).*cos(x1(t))+10(Global calculate the following functions by genetic algorithms optimal value y(t)=(x2(t)-5.1/(4*pi*pi).*x1(t).^2+5/pi.*x1(t)-6).^2+10.*(1-1/(8*pi)).*cos(x1(t))+10)