simpower

依次输入exchange swap tianchongJ即可，若想修改全排列矩阵的大小，可在MATLAB中打开tianchongJ修改J和N。 (Enter exchange swap tianchongJ in turn can, If you want to modify the full array matrix size can be modified in MATLAB to open tianchongJ J and N.)

说明：  1．用Matlab产生正弦波,矩形波,以及白噪声信号，并显示各自时域波形图 2．进行FFT变换，显示各自频谱图，其中采样率，频率、数据长度自选 3．做出上述三种信号的均方根图谱,功率图谱,以及对数均方根图谱 4．用IFFT傅立叶反变换恢复信号，并显示恢复的正弦信号时域波形图 (1. Using Matlab generated sine wave, rectangular wave, as well as the white noise signal, and display their respective time-domain waveform of Figure 2. FFT to transform, showing their frequency spectrum, including sampling rate, frequency, data length of 3-on-demand. Made of the three signals in root-mean-square maps, power maps, as well as the number of root-mean-square map 4. Fourier Transform IFFT with the restoration of signals, and displays the sinusoidal signal the resumption of time-domain waveform)

说明：  基于MATLAB的正交幅度调制系统仿真基于MATLAB的正交幅度调制系统仿真(MATLAB-based Simulation of quadrature amplitude modulation quadrature amplitude modulation MATLAB-based simulation)

这是一个电力系统谐波检测的MATLAB仿真模型，采用的是基于瞬时无功功率理论的ip-iq法，效果很好(This is a power system harmonic detection MATLAB simulation model, based on the instantaneous reactive power theory ip-iq method, the effect is very good)

说明：  电动汽车模型，用于整车性能和功能仿真。便于学习、交流(Electric vehicle model for vehicle performance and function simulation. Easy to learn and communicate)

feed forward back propagation neural network

卡尔曼滤波跟踪算法,这个是跟踪结果的程序(Kalman filtering algorithm, is tracking the results of the procedure)

DS_CDMA的程序实现 主要分为同步和异步的程序代码，可以自己进行仿真(The program realization are divided into synchronous and asynchronous DS CDMA program code, can simulate ourselves )

QAM调制和解调的matlab实现，瑞利信道，高斯白噪声(QAM modulation and demodulation matlab implementation, Rayleigh channel, Gaussian white noise)

In this paper, we show how support vector machine (SVM) can be employed as a powerful tool for $k$-nearest neighbor (kNN) classifier. A novel multi-class dimensionality reduction approach, Discriminant Analysis via Support Vectors (SVDA), is introduced by using the SVM. The kernel mapping idea is used to derive the non-linear version, Kernel Discriminant via Support Vectors (SVKD). In SVDA, only support vectors are involved to obtain the transformation matrix. Thus, the computational complexity can be greatly reduced for kernel based feature extraction. Experiments carried out on several standard databases show a clear improvement on LDA-based recognition