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说明：  de Boor 求值算法求作二次和三次B-样条插值曲线，Hartley-Judd法确定节点矢量，使用基于基的运算的插值法求控制顶点(algorithm evaluates demand for secondary and cubic B-spline interpolation curve, Hartley-Judd determine node vector-based computing interpolation method for the control vertices)

wind connected to grid circuit

MATLAB利用线性矩阵不等式解决控制系统的观测器设计问题并求解出了正定矩阵 (MATLAB linear matrix inequality solve observer design the control system and solved a positive definite matrix)

灰色模型的MATLAB实现，用于预测模型，是数学建模中的常用模型。(GM(1,1)model matlab )

正交采样仿真程序，验证采样结果的正确性与调制解调方法(Quadrature Sampling simulation program to verify the correctness of the results of sampling with the modulation and demodulation method)

这是个人总结的各种聚类算法的优缺点比较，包括了常用的一些聚类算法，如层次聚类算法、分割聚类算法、基于约束的算法等(The comparison of various clustering algorithms by myself,including 5 common clustering algorithms)

工程电磁场数值计算大作业，解决空间磁场分布的有限元解法(Engineering electromagnetic numerical big job to solve the finite element solution space magnetic field distribution)

此为模式识别中Parzen窗法估计概率密度函数。 全部程序流程如下： 1、读取FAMALE.TXT文件把身高或体重给数组，并求x1的样本数N1和窗宽、体宽； 2、读取MALE.TXT文件把身高或体重给数组，并求x2的样本数N2和窗宽、体宽； 3、读取Test2.txt文件把对应的身高或体重给数组A并求A的样本数M； 4、利用Parzen窗法估计概率密度函数判别男女性别； 5、对本判别的错误率进行统计。 (This is the pattern recognition Parzen window method to estimate the probability density function . All program flow is as follows : 1 , read FAMALE.TXT file to height or weight to the array, and find the number of samples x1 N1 and window width , body width 2 , read MALE.TXT file to height or weight to the array, and find the number of samples x2 N2 and window width , body width 3 , read Test2.txt file to the corresponding height or weight requirements for the array A and A is the number of samples M 4 , the use of Parzen window method to estimate the probability density function of gender discrimination 5 , determine the error rate of the statistics . )

自己编写的一个量子BP神经网络，数据是利用ELM官方网站上给出的数据(quantum bp neural networks)

包括最小二乘法、SVM、神经网络、1_k近邻法,从先验概率中采样，计算权重，使用matlab实现智能预测控制算法。( Including the least squares method, the SVM, neural networks, 1 _k neighbor method, Sampling a priori probability, calculate the weight, Use matlab intelligent predictive control algorithm.)