autotunerPID

通过Gabor原子库对语音信号进行处理，用到匹配追踪算法，程序很好用，对各种语音信号的仿真实验都很成功(Gabor atoms is through the voice signal processing, matching pursuit algorithm is used, the program useful, on the simulation of various audio signals are successfully)

说明：  直接序列扩频通信系统建模与仿真 PN码序列产生代码（matlab)(PN code sequence generated code (matlab))

matlab讲义 下载压缩后可看 共分五章ppt 相当详细(matlab lecture Download compressed to see very detailed divided into five chapters ppt)

unitcommitment usig laglang reduse

基于Matlab的分类器程序，采用最小误差判别准则(Classification program which based on Matlab, minimum error criterion)

基于预测控制的永磁同步电机直接转矩控制，利用matlab/simulink平台建立了永磁同步电机直接转矩控制的仿真模型(Based on predictive control of permanent magnet synchronous motor direct torque control of permanent magnet synchronous motor was established based on matlab/simulink platform simulation model of direct torque control.)

小波滤波技术，实现信号和图像的滤波处理，相较中值滤波处理的效果更佳理想(Wavelet filtering technology can filter signals and images better than median filtering)

高信噪比实测数据线谱提取，使线谱提取的一种典型方法(high signal-to-noise ratio measured spectral line data extraction, spectral lines from a typical method)

数据关联是多目标跟踪的一项关键技术。JPDA是大家公认的多目标跟踪中性能较好的数据关联算法，它 认为量测和目标是一一对应的关联关系，但在许多实际情况中，量测和目标是多一多对应的关系。针对上述情况，该文提 出了广义概率数据关联算法(Generalized Probability Data Association，GPDA)。文中从理论上对这两种算法的性能进行了 详细分析，并利用Monte Carlo技术对其性能进行了仿真比较。(Data association is one of the key technologies in multi—target tracking．And JPDA is considered as the best da· ta association method．JPDA considers the association of measurements with targets is simply one-to-one problem．But in many practical cases，the association of measurements with targets will be multiple—to—multiple problem．For this case，a Generalized Probability Data Association(GPDA)algorithm is proposed in this paper．Furthermore，this paper analyzes the performance of these two algorithms theoretically．And we give the comparative analysis of those performances by using Monte Carlo method．)

使用stepwise方法选择因变量的基于AR模型的时间序列预测程序(This alogrithm can select the predictors using step-wise policy and forecast the time series based on AR model.)