zhihuanbijiaopwm

灰色预测模型称为CM模型，G为grey的第一个字母，M为model的第一个字母。GM（1，1）表示一阶的，一个变量的微分方程型预测模型。GM（1，1）是一阶单序列的线性动态模型，主要用于时间序列预测。 一、GM（1，1）建模 设有数列 共有 个观察值 对 作累加生成，得到新的数列 ，其元素 （5-1） 有： 对数列 ，可建立预测模型的白化形式方程， （5-2） 式中： ——为待估计参数。分别称为发展灰数和内生控制灰数。设 为待估计参数向量 则 按最小二乘法求解， 有： （5-3） 式中： （5-4） （5-5） 将（5-3）式求得的 代入（5-2）式，并解微分方程，有 （1，1）预测模型为： （5-6）(gray forecasting model called CM model, the G-gray of a letter, the M model for the first letter. GM (1,1), a band of a variable type of differential equation models. GM (1,1) is a sequence of single-band linear dynamic model, mainly for time series prediction. A GM (1,1) model series with a total value of observation for the cumulative production, to a new series of its elements (5-1) : The series, we can establish the prediction model albino form of equation (5-2) where :-- to question the estimated parameters. The development will be known as the gray and hygiene control within a few gray. Set parameters to be estimated according to Vector least squares method, are : (5-3) where : (5-4) (5-5) (5-3) that obtained in lieu of income (5-2)- and solutions differential equations, (1 1) Fore)

fortran的matlab代码,可以将fortran仿真结果可视化(fortran matlab code, you can visualize simulation results fortran)

matlab code for room impulse response

P & Q control of DFIG

通过internet远程对示波器通道数据进行采集与读取控制(through remote internet access for oscilloscope data acquisition and control Read)

应用matlab车牌识别，图中给出了实例，大家可以自己看(Application matlab license plate identification, the figure gives the example everyone can watch)

一种遗传优化神经网络的算法，里面包含有初始化函数,在运行程序前先将实验数据load进去。输入A =load( D:数据.txt ) 这里的D是你存储数据的位置(A genetic optimization of neural network algorithm, which contains the initialization function, before running the program to the experimental data in load. Enter the A =load (D: data.Txt) where D is your data storage location)

杂波边缘 统一数据 五种方法,CA,GO,OS,GOSGO等 仿真图(Unified data clutter edge five methods, CA, GO, OS, GOSGO other simulation Figure)

说明：  这个源代码是小世界网络模型MATLAB仿真源代码。(small-world network )

make gear toentation the image segm