MATHEMATIC

说明：  利用害明滤波器对白噪声信号进行加权,提高信噪比(Filter with the use of harm-ming weighted noise signal to improve signal to noise ratio)

先进PID控制中的积分分离PID算法源程序(Advanced PID control integral separate PID algorithm source code)

This is a Modified Sinusoidal PWM Technique for Gate drive os Inverters in MATLAB Simulink.

目标跟踪程序实现_对雷达采集的数据进行分析，然后得出目标的飞行轨迹，程序采用了卡尔曼滤波加当前统计模型(Target tracking procedures to achieve _ radar data collected for analysis, and then draw the target flight path, the program uses a Kalman filter plus current statistical model)

MVDR Beamforming wher in the interference direction null s put and the signal in the intended direction is maximised. The plots the beamformer response

基于SVM的人口检测 把SVM这种比较优化的分类器用于人口检测中去 以得到较好的效果(SVM-based detection of the population compared to such optimized SVM classifier to test for the population in order to obtain good results)

svm程序，下载后可以直接运行，但需要结合data数据。没有密码(svm program can be run directly after download, but requires a combination of data data. No password)

研究上课所讲谱分析方法，利用实验验证书中的结论，掌握各种谱分析方法，学会实验设计和实验结果分析。 实验内容： 所应用到的谱分析方法，包括： 1） 非参数化方法：周期图（直接法）、BT法（间接法），Welch平均周期图法 2） 参数化方法： RELAX、Capon 3） 空间谱估计：常见的DOA方法（Capon） (Experimental Objective: To study methods of spectral analysis class talking about the use of experimental verification of the conclusions of the book, to master a variety of spectral analysis methods, learn experimental design and experimental results. Experiment content: applied to the spectral analysis methods, including: 1) non-parametric methods: periodogram (direct method), BT method (indirect method), Welch average periodogram 2) parameterization: RELAX, Capon 3) spatial spectrum estimation: common of DOA method (Capon))

于慧敏版信号与系统第二版部分自己编的部分matlab程序代码... (YU Hui-min version of the second edition of part of the signal and system part of their series of matlab code ...)

程序包含的设置包括：列数、样本总个数、建模样本数、预报因子数、预报对象数、学习因子、 动量因子、训练次数、总体误差、隐节点数。(Contains the settings include: the number of rows, the total number of samples, sample size modeling, forecasting factor number, forecasting the number of objects, learning factor, momentum factor, training frequency, the overall error, hidden nodes.)