youhua

matlab numerical phantom and EM simulation FDTD algorithm in matlab

利用现代谱估计中的AR 模型求取仿真噪声信号和真实噪声信号的模型参数，然后利用这些参数求得信号的功率谱图，通过功率谱图定性地描述主噪声源和次噪声源。在利用信号功率谱图定性描述噪声源后，进一步地利用曲线相似度和曲线关联度定量的识别主噪声源和次噪声源(Use of modern spectral estimation of AR model to strike a realistic simulation of the noise signal and noise model parameters, and then use these parameters obtained by the signal power spectrum, power spectrum qualitatively described by the main noise source and the second noise source. In the use of qualitative description of the noise signal power spectrum of the source, the further use of the correlation curve and the curve of quantitative similarity identification of the main noise source and the second noise source)

kalman filter extended

数字图像处理程序：付里叶描绘子方法的实现(Digital image processing: Fourier descriptors method implementation)

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.)

股票数据读取,将股票的数据读取至matlab中(Stock input)

it s about locating TDOA algorithm

关于混沌Kolmogorov熵的计算程序，在前人基础上自己编写的。已经用过很多混沌时间序列K熵的计算。(On chaotic Kolmogorov entropy calculation procedure, based on predecessors have written. Has been used a lot of chaotic time series K entropy calculations.)

给定一个日期，求出该日为星期几（已知2002-3-28为星期四）。(Given a date, find the day of the week (known 2002-3-28 of Thursday).)

说明：  统计中的COSSO方法,一种很有效的方法(cosso with adaptive models)