Hamming

bOUCLE DE VITESSE DE LA PMSM VITE FAIT

通过matlab实现数据的时域图D代码呵呵呵呵(When implementing data via matlab domain map)

实现了粒子群优化bp神经网络在锅炉燃烧优化中的应用，有详细的代码解释，仿真图例，运行时将程序名改为程序内的function命，运行即可(Particle swarm optimization bp neural network applications in boiler combustion optimization, detailed code interpreter simulation legend, change the name of the program to function within the program life runtime can run)

模式识别工具箱，很好用，matlab原程序(pattern recognizition toolbox)

MATLAB小波变换在发动机故障、齿轮故障诊断中应用的三个程序代码，非常实用(Three application of the MATLAB wavelet transform engine failure, gear fault diagnosis code, very useful)

Debye级数展开模型，计算均匀球形粒子散射角——散射光强曲线： 1. RB1.m 计算第一类Ricatti-Bessel函数的函数 2．RB2.m 计算第二类Ricatti-Bessel函数的函数 3. Alegendr.m 计算角函数 和 的函数 4. MieCoeff.m 计算米尔系数的函数 5. Amp.m 计算散射光复振幅的函数 6. DebyeIntensity.m Debye级数展开模型画均匀球形粒子散射角-光强图 (Debye series expansion model to calculate the uniform spherical particles scattering angle- scattering intensity curve: 1. RB1.m calculated first class Ricatti-Bessel function of the function 2. RB2.m calculate the second category Ricatti-Bessel function of the function 3. Alegendr.m computing angle functions and function 4. MieCoeff.m Mill coefficient calculation function 5. Amp.m calculate the scattering amplitude recovery function 6. DebyeIntensity.m Debye series expansion model painted uniform spherical particles scattering angle- light intensity diagram)

multi-level voltage svpwm space vector control algorithm for motor controlmulti-level voltage svpwm space vector control algorithm for motor controlmulti-level voltage svpwm space vector control algorithm for motor controlmulti-level voltage svpwm space vector control algorithm for motor control

说明：  二维光子晶体中，用平面波展开法求晶体能带图(In two - dimensional photonic crystal, using the plane wave expansion method to find the crystal band diagram)

这是数字信号处理的作业的第一部分包括了lms算法的基本步骤(This is a digital signal processing operations, including the first part of the basic steps of the lms algorithm)

斯坦福大学公开课第三课中涉及的局部加权线性回归，用matlab实现了在不同带宽下的对比(Stanford open class third lesson involved locally weighted linear regression, using matlab to achieve a comparison of different bandwidths)