a1xiangganjilei

By analyzing characteristics of Matlab and Visual C++,the advantages of applying the Matlab and Visual C++ interface to develop application program is presented. On basis of comparing with the other interface methods ,the advantages and disadvantages of several common interfaces are studied,and the most functions provided by Matlab engine is proved to be comprehensive. An example showing the combination of Matlab and VC in image and data processing is proposed

Euler method for differential equation

有借鉴意义哦，用MATLAB实现的压缩传感，空间目标识别，采用PM算法。( There are reference Oh, Using MATLAB compressed sensing, Space target recognition algorithm using PM.)

dhmm识别的matlab程序，使用matlab工具箱，有参考价值。(DHMM identification of Matlab procedures, the use of Matlab toolbox, a reference value.)

灰色系统GM（1，1）模型及其检验,用于时间序列预测(grey system GM（1，1）model and its testing，can be used to predict)

编写了MATLAB程序生产和扩频码进行仿真，仿真程序从发送方完成通道的接收端，得出在不同的扩频码，在不同的信噪比即误码率的图形比较直接序列扩频通信系统原理，改善系统性能的度(Written MATLAB program to produce and direct sequence spread spectrum communication system principle of spreading codes for simulation, emulation program to complete the channel from the sender to the receiver end, draw graphics comparison at different spreading code, the bit error rate under different SNR i.e., the degree of improvement of the system performance)

计算方法与实习大作业计算方法与实习大作业计算方法与实习大作业(Great job and internship calculation method and calculation of pupil work great work and practice)

mimo通信系统中空时编码处理书中所附的matlab仿真代码，供大家学习(mimo communication system of hollow-coded handle the book accompanying Matlab simulation code, for your learning)

基于MATLAB的ARIMA模型的源代码。ARIMA模型是自回归滑动平均求和模型，是时间序列分析模型，可以用于时间序列的预测。该代码实现了ARIMA模型的建模和谱分析过程(The ARIMA model based on MATLAB source code. ARIMA model is the sum of autoregressive moving average model is time series analysis models, can be used for time series prediction. The code to achieve the ARIMA models and spectral analysis of the modeling process)

从数据学习贝叶斯网络结构的蚁群优化实现 matlab平台下(Ant Colony Optimisation implementation for learning Bayesian Network structures from data)