fmrlc_tanker

这是基于PCA的人脸识别，用MATLAB编写，包含了K-L变换，奇异值分解等方法，且采用了最小距离分类器(This is based on the PCA face recognition, using MATLAB to prepare, including the KL transform, singular value decomposition and other methods, and the use of the minimum distance classifier)

EPS小日本的东西挺好的 EPS小日本的东西挺好的(Good little things EPS of EPS in Japan Japan' s something very good small)

惠更斯-菲涅尔衍射计算，非常不错，简单易行。(Huygens- Fresnel diffraction calculations, very good, easy.)

一种快速多目标遗传算法，NSGA-Ⅱ是目前最流行的多目标进化算法之一，它降低了非劣排序遗传算法的复杂性，具有运行速度快，解集的收敛性好的优点，成为其他多目标优化算法性能的基准。(A fast multi-objective genetic algorithm, NSGA-Ⅱ is the most popular one multi-objective evolutionary algorithm, which reduces the non-dominated sorting genetic algorithm complexity, running speed, the solution converges and good benefits set to become Other multi-objective optimization algorithm performance benchmarks.)

implementation of windows for filters using matlab code with examples

粒子群优化算法 matlab程序运行良好(Particle Swarm Optimization algorithm matlab program run)

这里讲述的是matlab的数据的统计描述与分析问题，含有多个程序可提供给大家参考参考 (To talk about is the statistical description and analysis of the problem of the Matlab data containing multiple programs are available to everyone REFERENCES)

Kalman 滤波 和曲线拟合 之间的对比试验(Kalman filtering and comparison test between the curve fitting)

已知一个序列x(n)=0.5cos(0.55*pi*n)+cos(0.45*pi*n),时域FFT分析其频谱。 （1）使用不同宽度的矩形窗截断该序列为M点，取M分别为：20，40，160,观察不同长度对频谱影响；（2）使用哈明窗和凯泽窗重做；（3）对三种窗的结果进行分析比较；（4）总结窗类型和长度对频谱分析的影响。 (Known a sequence x (n) = 0.5cos (0.55* pi* n)+ Cos (0.45* pi* n), time-domain FFT analysis of its spectrum. (1) use a different width of the rectangular window of the sequence for M cut-off point, take M are as follows: 20,40,160, observing the impact of different lengths of the spectrum (2) the use of Hamming window and the Kaiser window redo (3 ) on the three windows analyzed and compared the results (4) summarize the type and length of window for spectrum analysis.)

DWT的分解与逆变，可以用于风电和负荷预测之前的去噪点(decomposition and inverter which based on DWT , it can be used in the wind power prediction and load forecasting data )