XLTH

采用matlab进行数值的逼近，用神经网络实现的。 (Using matlab numerical approximation, using neural network realization.)

forward 1 dimension MT with examples

KSVDBox - Implementation of the K-SVD and K-SVD denoise algorithms.(K-SVD dictionary training. ksvd - K-SVD dictionary training. Signal denoising. ompdenoise - OMP denoising. ompdenoise1 - OMP denoising of 1-D signals. ompdenoise2 - OMP denoising of 2-D signals. ompdenoise3 - OMP denoising of 3-D signals. ksvddenoise - K-SVD denoising. Overcomplete DCT dictionary. odctdict - Overcomplete DCT dictionary. odct2dict - 2-D overcomplete DCT dictionary. odct3dict - 3-D overcomplete DCT dictionary. odctndict - Multi-dimensional overcomplete DCT dictionary. Demonstrations. ksvddemo - K-SVD training demonstration. ksvddenoisedemo- K-SVD denoising demonstration. Miscellaneous. showdict - Display a dictionary of image patches. ksvdver - K-SVD toolbox version information.)

包含代码的基于元胞自动机的人员疏散对冲模拟文章(Cellular Automata-based Room Evacuation Simulation Paper with Code)

包含wcdma的上下行链路仿真，适用于无线通信仿真开发和学习(Contains wcdma uplink and downlink simulation, simulation development for wireless communication and learning)

对并联机构所受到的外力进行初始值赋值，并进行并联机构的部分构件分析(Some elements of the analysis of parallel institutions by external initial value assignment, and the parallel mechanism)

基于对蓄电池充电，并且能够实现逆变包含SVPWM模块，而且包含DC/DC模块。可实现整流逆变双向。(Based on the battery charge, and can achieve SVPWM inverter module contains, and includes DC/DC module. Enables bidirectional rectifier inverter.)

一个很全的matlab工具包，你肯定需要！(a whole tool kit of Matlab, you definitely need!)

一个会动的五角星,应用matlab软件编程。(A moving star，using matlab program.)

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 )