niiicode

异步电机矢量控制仿真模型，比较基本，已通过测试，完全好用。(Vector control induction motor simulation models, more basic, has been tested to be fully useful.)

离散算法，硕士论文一部分，希望对大家有所帮助。(Discrete Algorithms, part of a master's thesis, we want to help.)

MATLAB电机仿真精华50例PDF+源码(MATLAB simulation of the essence of 50 cases of motor PDF+ source code)

Implement the K nearest neighbor algorithm by your own instead of using available software. 2. Use K-fold cross validation to generate training and testing datasets. You should try different K values (3~8) to see how they affect your result. 3. Train the classifier using your training dataset, and test the classifier using your testing dataset. 4. Repeat the experiment (Step 2 and Step 3) 30 times. For each time, you need to record the training data accuracy and testing data accuracy. Finally, you can obtain the average training data accuracy and average testing data accuracy.

葛哲学编著《小波分析理论与MATLAB R2007实现》的所有源代码。(Edited by GE philosophy )

wavelet transformation

计算孔口应力集中的计算复变函数方法。本人自己编的程序，可读性比较差。(Calculation of stress concentration calculation orifice complex variable function method. I own procedures, a relatively poor readability.)

说明：   改进的牛顿法求解： 方程的构造方法:给出[0,1]区间上的随机数(服从均匀分布)作为方程的根p*. 设你的班级数为a3,学号的后两位数分别为a2与a1,从而得到你的三次方程 例如:你的31班的12号,则你的方程是21x3+60x2+2x+a0=0的形式. 方程中的系数a0由你得到的根p*来确定. (improve Newton's method : Construction of the equation : given interval [0,1] on the random number (subject to uniform distribution) as the root equation p*. set up your classes at a3, after learning of the double-digit for a1 and a2, so you get the three equations for example : Your 31 classes on the 12th, then your equation is 21x3 60x2 2x a0 = 0 forms. the equation coefficients a0 you get from the root of p* to determine.)

计算AR模型的参数，包括滤波、去趋势化、AIC判断阶数、计算AR模型系数。(Calculation AR model parameters, including filtering, detrended, AIC judgment order to calculate the AR model coefficients.)

The timing_recovery_1.mdl uses Simulinks variable step solver to create a continous time QPSK signal with both carrier and symbol rate errors. This is a relativly straight forward approach, but is computationally expensive. The sescond model, timing_recovery_2Q.mdl uses a purely discrete time approach using the same fractional sample delay approach as does the receiver. This can run much faster.