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Matlab 牛顿插值法
- x=a:(b-a)/n:b; %插值节点 y=f(x); plot(x,y,'b') %用蓝色线作被插函数图象 hold on z=a:(b-a)/(2*n):b; n=length(x); for j=2:n for i=n:-1:j y(i)=(y(i)-y(i-1))/(x(i)-x(i-j+1));%计算差商 end end u=y(n); m=length(z); for j=1:m for i=n-1:-1:1 u=y(i)
abdi-PCA4Wiley.zip
- ua University, in 2002 publi this document, including the Mont A program of curve fitting based Bayesian Filter. Bayesian (Bayesi a target tracking system MATLAB s cubic spline curve fitting This i book is widely used in engineerin this
AmbiguityFunction
- 建立并绘制任意信号的模糊函数。已在巴克码，线性调频，脉冲信号中测试过。-This program estimates and plots the ambiguity function for any signals. I have tested it for the Barker code, chirp signal, pulse signal. for good results the length of the input vector (Ut)should be more than 7.
signal_ambiguity_function
- 这个源码可以分析绘制多种信号的模糊函数，已经试验过巴克码，线性调频信号，脉冲信号等-Descr iption: This program estimates and plots the ambiguity function for any signals. I have tested it for the Barker code, chirp signal, pulse signal. for good results the length of the input vector (Ut)sho
bubbleplot3
- 在三维空间中画泡泡,可以指定在三维空间中的x,y,z的坐标以及泡泡的半径r和颜色c(RGB).-BUBBLEPLOT3(x,y,z,r), where x, y, z and r are four vectors of the same length, plots bubbles of radii r in 3-space with centers at the points whose coordinates are the elements of x, y and z. If r
circle_fit
- 圆拟合，通过已知的多组数据，拟合出最小误差的圆-Given a set of measured x,y pairs that a re supposed to reside on a circle, but with some added noise. A circle to these points, i.e. find xc,yc,R, such that (x-xc)^2+(y-yc)^2=R^2
gradiente
- Calculate a gradient by using a mask (sqrt(R² + I² ).
ComprehensiveWeldingSimulation
- 一个阻感回路电流电压波形仿真源码，可以实现simulink模块里不可控的参数调整。-This code includes the U-I simulation in R-L circuit such as welding SCR component。
Metaheuristic
- M e t a h e u r i s t i c 算法的实例，-M etaheuristic algorithm instance,
ols
- 正交最小二乘辨识算法 该算法除了实现最小二乘辨识功能之外而且能按照各项重要性将其逐一选出并且估计相应系数-OLS Orthogonal Least Quares. [x, ind] = OLS(A,b,r) gives the solution to the least squares problem using only the best r regressors chosen from the ones present in matrix A. This
publicationdtl
- S u r f e r自动控制技术在气象资料 自动成图中的应用 -Ab s t r a c t ： The ma i n f un c t i o n s o f Su r fe r s o f t wa r e，Ac t i v e X a u t o ma t i o n t e c h ni q ue a nd t he i n t e r fa c e o f VB a p p l i c a t i o n a n d S u r
HoughObject
- Circles We can extend the Hough transform to other shapes that can be expressed parametrically. For example, a circle of fixed radius can be described fully by the location of its center (x, y). Think of each feature (edge) point on the circle
luv2rgb
- LAB2RGB Convert an image from CIELAB to RGB function [R, G, B] = Lab2RGB(L, a, b) function [R, G, B] = Lab2RGB(I) function I = Lab2RGB(...)- LAB2RGB Convert an image from CIELAB to RGB function [R, G, B] = Lab2RGB(L, a, b) f
6
- read an image then draw it s 3D Histogram,include R and G or R and B or G and B. I have never found correct code of this before
Verzani-SimpleR
- This book is verzari simple R programm. I think this book is really helpful.
Ten_classical_algorithm_of_mathematical_modeling.r
- 数学建模十大经典算法，希望各位喜欢。请多多支持，多多下载！-Ten classical algorithm of mathematical modeling, I hope you like. Please support, a lot of downloads!
output_of_DCT_in_image_fusion
- u can calculate DCT clc inp = imread( Im1.jpg ) inp1 = imread( Im2.jpg ) A = double(inp(:,:,1)) B = double(inp1(:,:,1)) A1=double(blkproc(A,[8 8], dct2 )) B1=double(blkproc(B,[8 8], dct2 )) [r,c] = size(A
fit_maxwell_pdf
- fit_maxwell_pdf - Non Linear Least Squares fit of the maxwellian distribution. given the samples of the histogram of the samples, finds the distribution parameter that fits the histogram samples. fits data to the probability of the form:
fit_ML_laplace
- fit_ML_normal - Maximum Likelihood fit of the laplace distribution of i.i.d. samples!. Given the samples of a laplace distribution, the PDF parameter is found fits data to the probability of the form: p(x) = 1/(2*b)*exp(-abs(x-u)/b)
fit_ML_log_normal
- fit_ML_normal - Maximum Likelihood fit of the laplace distribution of i.i.d. samples!. Given the samples of a laplace distribution, the PDF parameter is found fits data to the probability of the form: p(x) = 1/(2*b)*exp(-abs(x-u)/b)