Modelling_of_PMSM

说明：  本程序是用MATLAB做的玫瑰曲线，效果如玫瑰花一样(This program is a rose curve made with MATLAB, the effect is like a rose)

fft算法的具体实现，应用基2法实现，有比较好的实验结果。(fft concrete realization of the algorithm, the application base 2 method achieved a relatively good results.)

说明：  αβγ滤波器，αβ滤波器，卡尔曼滤波器仿真实例。(Simulation examples of alpha beta gamma filter, alpha beta filter and Kalman filter.)

说明：  matlab中一些重要的课件和资料，比较有用啊！(matlab courseware and some important information, more useful ah!)

用GUI编写的OFDM代码,编写质量高,是学习的好程序,内有doc详细解释。(prepared using OFDM GUI code, preparation of high quality, is a good learning process, there doc explained in detail.)

说明：  通过分析交通流量时间序列的特点，引入BP神经网络进行短时交通流预测。首先，分析了短时交通流量预测的意义及研究背景；然后，介绍了BP神经网络的结构模型、学习规则以及BP算法的改进算法；最后，通过BP神经网络对短时交通流进行预测，并分析了在各种不同条件下的预测情况。(through the analysis of the characteristics of traffic flow time series, introduces BP neural network for short-term traffic flow prediction. First of all, it analyzes the significance of short-term traffic flow prediction and research background. And then, it has discussed the model and structure, the study rule and the improved algorithm of BP neural network. Finally, by BP neural network to forecast the short-term traffic flow, it analyzes under different conditions of the neural network prediction.)

直方图均衡。 不利用MATLAB自带的histeq.m函数，自己编写主函数进行直方图均衡化处理(hist equal)

rate range gyro balanced system control

将NSGA-II 的理念融入粒子群中，并加入保存优秀解的机制，提出MPSO算法。针对MPSO 易陷入局部极值的缺点，加入变邻域机制，通过随机的破坏旧解和重建新解，找到全局最优解，以达到帮助粒子跳出局部极值的目的，提 出VNS-MPSO 算法。(Will the NSGA- II concept into particle swarm, and join save good solution mechanism, MPSO algorithm is put forward. Against disadvantages of MPSO easy to fall into local extremum, join the variable neighborhood mechanism, through the random destruction of the old and rebuild the new solution, find the global optimal solution, to achieve the purpose of help particles jump out of local extremum, The VNS- MPSO algorithm.)

滤波反投影的CT图像重建算法、人脸识别中核心算法(Filtered back-projection of the CT image reconstruction algorithm, the core algorithm for face recognition)