matlab

说明：  利用子程序fast-lot.m,实现对该图像的变化及压缩，并和以为信号做dct后的图像质量相比较。(Using subroutine fast-lot.m, to achieve the changes and compression of the image, and after, and that the signals do dct compare the image quality.)

matlab 计算发动机点火提前角的实验计算程序(Matlab, the advanced ignition Angle of experimental applications)

emd分解程序，和hht变换程序，适合初学者，希望对大家有用。(emd decomposition process, and the hht transformation program, suitable for beginners, and hope to be useful.)

利用求根MUSIC算法进行ＤＯＡ估计的源程序(Root MUSIC algorithm using the DOA estimation of the source)

1.灰色预测领域的权威书籍，详细介绍灰色预测模型。 2.经典灰色预测案例，并附有matlab操作程序，简单明了。(1., gray prediction authority books, detailed description of the gray forecasting model. 2. classic gray forecasting case, and accompanied by MATLAB operating procedures, simple and clear.)

马尔科夫前向算法，马尔科夫链算法，马尔科夫一阶算法 (forward algorithm)

在遗传算法的基础上，提出了改进，即混合遗传算法。(Based on the genetic algorithm, proposed the improvement, i.e., hybrid genetic algorithm.)

Genetic algorithm feature selection by using Logistic Regression

灰色 GM(1 ,1) 预测模型是灰色理论中的重要组成部分 ,也是主要的预测方法之一. 因此 , GM(1 ,1)模型的应用范围很广泛.,用 MATLAB 软件 实现了 GM(1 ,1)预测算法并给出了源代码.(Grey GM (1, 1) prediction model is an important part of grey theory, and it is also one of the main prediction methods. Therefore, GM (1, 1) model has wide application scope. GM (1, 1) prediction algorithm is implemented by MATLAB software, and the source code is given.)

位置指令为幅值为1.0的阶跃信号，r（k）=1.0。网络结构为1-4-1，高斯函数的参数值取Cj=[-2 -1 1 2]T ，B=[0.5 0.5 0.5 0.5]T 。 网络权值学习参数为η=0.30，α=0.05 。PID控制各参数的初RBF网络控制，被控对象为G（s）= 取采样时间为1ms，采用Z变换进行离散化，离散化后的被控对象为 y(k)=-den(2)*y（k-1）-den(3)*y(k-2)+num(2)*u(k-1)+num(3)*u(k-2) 始值为，kp=20， kd=0.3， ki=0.1。 (Position command for the step signal amplitude of 1.0, r (k) = 1.0. The network structure is 1-4-1, the Gaussian function parameters taken Cj = [-2-1 1 2] T, B = [0.5 0.5 0.5 0.5] T. Weight learning parameter η = 0.30, α = 0.05. PID control parameters at the beginning of each RBF network control, the controlled object is G (s) = take the sampling time is 1ms, using the Z transform discrete, discretized controlled object is y (k) =-den (2)* y (k-1)-den (3)* y (k-2)+num (2)* u (k-1)+num (3)* u (k-2) initial value, kp = 20, kd = 0.3, ki = 0.1.)