simulink_sfcl-master

模式识别最大最小距离算法实现，matlab实现的，可以直接运行(Pattern recognition of maximum and minimum distance algorithm to achieve the Matlab)

《matlab40个经典案例》书本PDF扫描版和源程序。福利继续上。欢迎指教(" Matlab40 a classic case of" book PDF scan version and source code. Benefits continue on. Welcome advice)

画跟踪图 Painting Tracking Chart(Painting Tracking Chart)

vblast，迫零检测算法和消除干扰的迫零检测算法误帧率对比图(vblast zero forcing detection algorithm and to eliminate the interference zero forcing detection algorithm frame error rate comparison chart)

应用对偶四元素进行组合导航解算，包括姿态解算，速度解算，位置解算(the caculation of strapdown-inertial navigation with dual-quaternion)

利用BP分类器对烟雾图像进行识别，并将结果存储到TXT文档中。(BP classifier use smoke image recognition, and stores the result TXT document.)

SVPWM是近年发展的一种比较新颖的控制方法，是由三相功率逆变器的六个功率开关元件组成的特定开关模式产生的脉宽调制波，能够使输出电流波形尽可能接近于理想的正弦波形。空间电压矢量PWM与传统的正弦PWM不同，它是从三相输出电压的整体效果出发，着眼于如何使电机获得理想圆形磁链轨迹。SVPWM技术与SPWM相比较，绕组电流波形的谐波成分小，使得电机转矩脉动降低，旋转磁场更逼近圆形，而且使直流母线电压的利用率有了很大提高，且更易于实现数字化。(SVPWM is a novel control method developed in recent years. It is a pulse width modulation wave generated by a specific switching mode consisting of six power switching elements of three-phase power inverters, which can make the output current waveform as close as possible to the ideal sinusoidal waveform. Space voltage vector PWM is different from traditional sinusoidal PWM. It is based on the overall effect of three-phase output voltage, focusing on how to obtain the ideal circular flux trajectory of the motor. Compared with SPWM, the harmonic component of winding current waveform is smaller, which reduces the torque ripple of motor, makes the rotating magnetic field more close to the circle, and improves the utilization ratio of DC bus voltage greatly, and makes it easier to realize digitization.)

本文件是一个简单的S-function来算出realtime下将信号加倍的(This document is a simple S-function to calculate double realtime signal under)

说明：  讲述基于QPSK调制在高斯，瑞利，CorrRayleigh信道下的误码分析。(About QPSK modulation in the Gaussian, Rayleigh, the CorrRayleigh channel bit error analysis.)

聚类算法，不是分类算法。分类算法是给一个数据，然后判断这个数据属于已分好的类中的具体哪一类。聚类算法是给一大堆原始数据，然后通过算法将其中具有相似特征的数据聚为一类。这里的k-means聚类，是事先给出原始数据所含的类数，然后将含有相似特征的数据聚为一个类中。所有资料中还是Andrew Ng介绍的明白。首先给出原始数据{x1,x2,...,xn}，这些数据没有被标记的。初始化k个随机数据u1,u2,...,uk。这些xn和uk都是向量。根据下面两个公式迭代就能求出最终所有的u,这些u就是最终所有类的中心位置。(Clustering algorithm, not a classification algorithm. Classification algorithm is to give a figure, and then determine the data belonging to a specific class of good which category. Clustering algorithm is to give a lot of raw data, and then through the algorithm which has similar characteristics data together as a class. Here k-means clustering, is given in advance the number of classes contained in the raw data, then the data contain similar characteristics together as a class. All information presented in or Andrew Ng understand. Firstly, raw data {x1, x2, ..., xn}, the data is not labeled. K random initialization data u1, u2, ..., uk. These are the vectors xn and uk. According to the following two formulas can be obtained final iteration all u, u is the ultimate all these classes the center position.)