fast-matlab-code

Eight psk communication system

转换色彩空间，实现各种图像的Compress-Decompress(Color space conversion, to achieve a variety of image Compress-Decompress)

MATLAB_SIMULINK在电力系统工程仿真中的应用，本书对电力系统仿真的各元器件进行了详细的介绍，同时采用大量案例，学习simulink在电力系统仿真很容易上手。(MATLAB_SIMULINK in power systems engineering simulation applications, this book on the power system simulation carried out for each component in detail, while using a large number of case study in the power system simulation simulink very easy to use.)

获取图象并直接在GUI的界面上显示，Matlab代码实现了一个调制的GUI界面，实现BPSK调制算法的源代码。(To acquire images directly in the GUI interface, Matlab code implements a GUI interface modulation BPSK modulation algorithm source code.)

基于异步电动机的矢量控制模块，应用于中压船舶电力推进系统，可实现6KV船舶电网推进系统的矢量控制功能(VC contrl)

说明：  线阵天线方向图仿真M文件，七元线形天线阵(Linear array antenna directed graph simulation M files)

利用matlab对汽车加速性能进行仿真，并行自动代码生成和测试(Accelerating Simulink Optimization, Code Generation & Test Automation Through Parallelization )

卡尔曼滤波同滑动平均法滤波的比较 附波形图(Kalman filter with a sliding average filter with waveforms)

TSP问题蚁群算法通用MATLAB源程序(TSP problem ant colony algorithm universal MATLAB source)

城市交通流的运行存在着高度的复杂性、时变性和随机性,实时准确的交通流预测是智能交通系统,特别是先进的交通管理系统与先进的出行者信息系统研究的关键. 基于交通流预测的特点,给出了基于遗传算法的小波神经 网络的交通预测模型GA-WNN ,用具有自然进化规律的遗传算法来对小波神经网络的连接权值和伸缩平移尺度进行前期优化训练,部分代替了小波框架神经网络中按单一梯度方向进行参数优化的梯度下降法,克服了单一梯度下降法易陷入局部极小和引起振荡效应等缺陷. 仿真实验验证了GA-WNN 预测模型对短时交通流的预测的有效性.(For the high complexity ,time-variation and probability of urban traffic flow , its real-time and exact prediction is critical to the research of intelligent traffic systems , especially for the advanced traffic manage-ment system and advanced traveler information system. Based on the character of the traffic flow prediction , a GA-WNN model is given based on the wavelet neural network with genetic algorithm. The genetic algorithm of natural evolving law for the gradient descendent algorithm in Wavelet Neural Network is partly substituted to pre-optimize the connection weight and the extension scale of the wavelet neural network and later optimize the parameters along a single gradient vector. This method overcomes some drawbacks when there exists a single gradient descendent algorithm , such as local minimum and oscillation. A short-time traffic flow prediction sim- ulation using the GA2WNN prediction model demonstrates the validity of the model .)