nsctdec

该系统使用极其简便，即使 你对各种网络模型不是很深刻的了解，也可以很好的使用该系统。使用时， 你可以自己修改网络的各种参数，交互性较好，而且该系统通过大量的图示 及参数设置，可以让你了解每个应用实例实现的过程及详细步骤。(The system is extremely easy to use, even if you have to various network model is not very profound understanding can be a good use of the system. When used, you can change the network's own parameters, interactive better, but the system through a lot of icon and parameter settings, so you can understand each application process and the realization of detailed steps.)

matlab codes for various signals generation

说明：  灰色预测是就灰色系统所做的预测。所谓灰色系统是介于白色系统和黑箱系统之间的过渡系统。(Grey prediction is the prediction of grey system. The so-called grey system is a transitional system between the white system and the black box system.)

这是用MATLAB编写的模拟退火算法求解背包问题的程序(This is prepared using MATLAB algorithm simulated annealing procedure for Knapsack Problem)

本压缩包包含（1、可编程控制器的编程指令2、运料小车控制模拟3、相鼠笼式异步电动机星/三角换接启动控制4、四节传送带的模拟）(This compressed package contains (1, 2 programmable controller programming instructions, material transport car control simulation 3-phase Squirrel Cage Induction Motor Star/Delta Start Control for 4 then, four belts of simulation))

关于非线性离散系统辨识，解耦，恢复原信号，BP神经网络的整个训练过程。( Nonlinear discrete system identification, Decoupling, restore the original signal, The entire training process BP neural network.)

MATLAB与C、c++语言混合编程方法介绍的经典文章(MATLAB and C, c++ programming language introduced the classic mixed-article)

说明：  4PSK的源程序 matlab程序示例(4PSK source of the examples Matlab)

matlab gui中ActiveX控件使用(the introduction of ActiveX in matlab gui)

ICP fit points in data to the points in model. Fit with respect to minimize the sum of square errors with the closest model points and data points. Ordinary usage: [R, T] = icp(model,data) INPUT: model - matrix with model points, data - matrix with data points, OUTPUT: R - rotation matrix and T - translation vector accordingly so newdata = R*data + T . newdata are transformed data points to fit model see help icp for more information (ICP fit points in data to the points in model. Fit with respect to minimize the sum of square errors with the closest model points and data points. Ordinary usage: [R, T] = icp(model,data) INPUT: model- matrix with model points, data- matrix with data points, OUTPUT: R- rotation matrix and T- translation vector accordingly so newdata = R*data+ T . newdata are transformed data points to fit model see help icp for more information )