KalmanAll

Fast Fourier transform matlab code foile

计算网络效率，输入参数，网络的邻接矩阵，输出任意节点对间的效率，及网络效率。 设计思路：floyd算法求最短路径，求倒数，求平均值(Computing network efficiency, input parameters, the network' s adjacency matrix, any node between the output efficiency, and network efficiency. Design ideas: floyd shortest path algorithm, a reciprocal, an average)

说明：  用于单变量的偏最小二乘回归，直接给出回归系数。 X0是自变量矩阵，Y0是因变量向量，PRESS是去除其中某一个样本而回归出的方程对 缺失样本的误差估计，SS则是使用全部样本回归出的方程对同一个样本的误差估计。 在h个成分时，计算PRESS_h与SS_h-1，若1-PRESS/SS大于0.0975时，应增加成份。 [row,col]=size(pz) (For single-variable partial least squares regression, regression coefficients are given directly. X0 is the independent variable matrix, Y0 is the dependent variable vector, PRESS is to remove one of them out of the sample and the regression equation for missing samples error estimates, SS is the use of all of the sample regression equation out of a sample of the same error estimates. In h constituent, the calculation PRESS_h and SS_h-1, if the 1-PRESS/SS greater than 0.0975, to increase the ingredients. [Row, col] = size (pz) )

用于计算多体系统动力学的程序,可用于多体系统动力学的计算和仿镇,也可用于机器人的设计和计算(used in the calculation of multi-body system dynamics procedure can be used for multi-body system dynamics calculations and imitate town, Robots can also be used for the design and calculation)

乳腺图像轮廓提取，配准和分割的预处理工作(breast registration)

var模型的实现。针对var模型的算法，估计模型的参数。并对其进行检验(achieve var model. Var model for the algorithm, the estimated parameters of the model. And its test)

假彩色图像变换，分别求图像的R、G、B三个通道的图像(False color image transformation, image of R, G, B three channel of the image)

对Matlab的基础学习，是初学者可以迅速对Matlab上手(Matlab based on learning, beginners can quickly get started on the Matlab)

通过实际的例子学习simulink中s函数的使用及编写(Learning through practical examples of the use of the simulink s in function and write)

两种噪声下，卡尔曼滤波的100次Monte Carlo仿真验证，画出了每时刻卡尔曼预测均方根误差和估计均方根误差的Monte Carlo变化曲线(with uniform and Gaussian noise respectively,through 100 Monte Carlo simulations,output thecurves of sampled state prediction variances and estimate variances of kalman filter in each element vs.time step.)