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0qpsk the matlab simulation program, including modulation, demodulation and description

--次端点镜像延拓法获得的上下包络线； --注：输入参数是由MaMin函数获得的极值点；( - Second endpoint mirror extension obtained upper and lower envelope - Note: The input parameter is obtained by the extreme points MaMin function )

该代码是求解分数阶线性常微分方程的代码，但是输入有限制(This function solves linear fractional-order differential equation)

这是GM（1,1）灰色预测的原理，为PDF格式。可进行对一些数据的预测工作。(This is the principle of GM (1,1) gray prediction is in PDF format. Forecasting can be carried out on some of the data.)

本程序是自己写的，经过验证的，瑞利衰落信道下的BPSK信号仿真(This program is written in their own, after verification, simulation of BPSK signals in Rayleigh fading channels)

说明：  基于灰色预测，指数平滑预测的几个MATLAB程序，对于预测类问题的数学建模，可以说是必备程序。我的导师给我传的。 funesm1,2,3分别为一二三次指数平滑预测程序 fungry为灰色预测程序 funcoef为时间序列预测程序 pudn为广大的编程爱好者提供了一个交流的平台，我在此先谢谢网站的工作人员，我也会努力编出好的程序，一起同大家分享。(Based on Gray predicted that the number of exponential smoothing forecasts MATLAB procedures, issues for the prediction of the mathematical modeling, can be said to be an essential procedure. My mentor gave me spread. funesm1, 2,3 were 123 times procedures fungry exponential smoothing forecast procedures funcoef gray prediction for time series forecasting procedures for pudn programming for the majority of fans provided a platform for exchange, I would like to thank the site staff, I will strive for a good program, to share together.)

这事很不错的半定规划matlab软件,this is the important software of SDP.(this is the important software of SDP.)

基于matlab实现的 文字识别设计(2) 附带图片(Matlab implementation of character recognition based on design (2) Poster)

说明：  求解非线性方程组，迭代法，数值分析，不动点迭代法(Solving nonlinear equations)

说明：  BP网络是通过输入层到输出层的计算来完成的。多于一层的隐含层虽然能在速度上提高网络的训练,但是在实际应用中需要较多的训练时间,而训练速度可以用增加隐含层节点个数来实现,因此在应用BP神经网络进行预测时,选取只有一个隐含层的三层BP神经网络就足够了。(The BP network is completed through the calculation of the input layer to the output layer. Although the hidden layer of more than one layer can improve the training of network in speed, more training time is needed in practical application, and the training speed can be realized by increasing the number of hidden layer nodes. Therefore, when the BP neural network is used to predict, the three layer BP neural network with only one hidden layer is sufficient.)