76552939sdr_model

普通的通信系统仿真平台的搭建，从发射端到接收端。(Ordinary communication system simulation platform structures, from the launch-ended receiver.)

simulink通信系统仿真，频分多址，时分多址，码分多址(simulink simulation of communication systems, frequency division multiple access, time division multiple access, code division multiple access)

样本熵的matlab源代码，可以求出样本的熵值(Sample Entropy)

说明：  To find the encryption and decryption of image which is affected by the noise.

bp神经网络的非线性系统建模————非线性函数拟合(nonlinear system modeling bp neural network nonlinear function fitting )

人眼定位MATLAB源程序人眼定位MATLAB源程序人眼定位MATLAB源程序人眼定位MATLAB源程序(MATLAB source localization of human eyes eyes Eyes Location Location MATLAB MATLAB source code source code source MATLAB Eyes Location)

计算井下矩形巷道中多径传输的接收点归一化功率的几何光学仿真(Underground tunnel in the calculation of a rectangular multi-path transmission normalized power receiving point of geometrical optics simulation)

基于SystemView的巴克码识别器的仿真与分析基于SystemView的巴克码识别器的仿真与分析(Based on the Barker Code Identifier SystemView of the simulation and analysis based on the Barker Code Identifier SystemView Simulation and Analysis)

GM(1,1)模型1-4 1：GM(1,1)模拟模型，在matlab中的输入方法为gm1（x），x指要模拟的序列。 2：GM(1,1)预测模型，在matlab中的输入方法为gm2（x，K），x指要模拟的序列，K指从以后序列第一个数据算起的第k个待预测数据。 3：GM(1,1)群模拟模型，在matlab中的输入方法为gm3（x），x指要模拟的序列。 4：GM(1,1)群预测模型，在matlab中的输入方法为gm4（x，K），x指要模拟的序列，K指从以后序列第一个数据算起的第k个待预测数据。 gm4对序列趋势比较好的数据预测效果较好，对上下变动的数据，特别是后4个数据趋势跟前面的数据相反的，预测效果很差。 gm2对上下变动的数据，预测效果比gm4好，但对趋势较好的数据，预测精度没有gm4高。 gm3比gm1模拟精度要高。 可以以x=[1 3 5 7 9 11 13 15]进行实验。x输入默认行向量。 所有程序在matlab6.0上调试通过。(GM (1,1) 1-4 1 : GM (1,1) simulation model Matlab in the input method for gm1 (x), x entail simulation sequence. 2 : GM (1,1) model, in the Matlab input method for gm2 (x, K), x entail simulated sequence, from the K refers to the first series after a run of data k pending forecast data. 3 : GM (1,1)- simulation model, in the Matlab input method for gm3 (x), x entail simulation sequence. 4 : GM (1,1)- forecasting model, in the Matlab input method for gm4 (x, K), x entail simulated sequence, from the K refers to the first series after a run of data k pending forecast data. Gm4 right sequence relatively good trend data predicted better results, the next change in the data, especially after four data with the previous trend data contrary, predict poor. Gm2 next to the change in forecast resu)

说明：  使用turboedit算法进行宽带组合和电离层组合的周跳探测，并修复。可以探测16个历元外的周跳(Using the turboedit algorithm for broadband combination and ionospheric combination of cycle jump detection and repair. Can detect 16 epoch outside the cycle)