代码搜索：Approximation
找到约 1,542 项符合「Approximation」的源代码
代码结果 1,542
www.eeworm.com/read/119864/6081819
c igamif.c
/* igamif()
*
* Inverse of complemented imcomplete gamma integral
*
*
*
* SYNOPSIS:
*
* float a, x, y, igamif();
*
* x = igamif( a, y );
*
*
*
* DESCRIPTION:
*
* Given y, th
www.eeworm.com/read/304876/6356587
m example2_3_1.m
load leleccum; %装载信号
s=leleccum(1:3920);
ls=length(s);
[cA1,cD1]=dwt(s,'db1'); %采用db1基本小波来分解信号
A1=upcoef('a','cA1','db1',1,ls); %第三步中产生的系数cA1和cD1构造第一层的低频和高频（A1和D1）系数
D1=upcoef('d','cD1','db1',1,l
www.eeworm.com/read/494076/6387750
m xnewgrnn.m
function xNewgrnn
% xNewgrnn.m
% 函数逼近(function approximation)--用函数NEWGRNN()和SIM()创建和仿真
% 普遍化回归神经网络(generalized regression neural network,GRNN)
%
% Author: HUANG Huajiang
% Copyright 2003 U
www.eeworm.com/read/488783/6485500
m wave.m
%一段小波分解的程序
load leleccum;%载入信号,leleccum为4320个元素
s=leleccum(1:3920);%设置变量
ls=length(s);
%对信号进行单步小波分解，采用db1小波进行单步分解，其中dwt函数是专门用来进行单步小波分解的
[ca1,cd1]=dwt(s,'db1');
%这样就得到了信号在尺度为1时小波分解的近似部分ca1和细节部分cd
www.eeworm.com/read/485466/6562976
m example2_3_1.m
load leleccum; %装载信号
s=leleccum(1:3920);
ls=length(s);
[cA1,cD1]=dwt(s,'db1'); %采用db1基本小波来分解信号
A1=upcoef('a','cA1','db1',1,ls); %第三步中产生的系数cA1和cD1构造第一层的低频和高频（A1和D1）系数
D1=upcoef('d','cD1','db1',1,l
www.eeworm.com/read/482655/6620702
m fftexample.m
%fft的应用举例
N=input('input N:');
T=input('input T:');
%compute the approximation of X(w)
t=0:T:2;
x=[t-1 zeros(1,N-length(t))];
Xk=fft(x);
gam=2*pi/N/T;
k=0:10/gam; %for plotting pruposes
Xapp
www.eeworm.com/read/477455/6736055
m xnewgrnn.m
function xNewgrnn
% xNewgrnn.m
% 函数逼近(function approximation)--用函数NEWGRNN()和SIM()创建和仿真
% 普遍化回归神经网络(generalized regression neural network,GRNN)
%
% Author: HUANG Huajiang
% Copyright 2003 U
www.eeworm.com/read/410134/11301029
m wtlsopt.m
function [x,info,dh] = wtlsopt(d,w,m,opt)
% WTLSOPT - Weighted Total Least Squares approximation
% by standard local optimization algorithms (Algorithm 2.4).
%
% [x,info,dh] = wtlsopt(d,w,m,opt)
%
% D
www.eeworm.com/read/410134/11301039
m mwtlsx.m
function [m,dh] = mwtlsx(d,w,x)
% MWTLSX - Weighted Total Least Squares misfit computation.
% [M,DH] = MWTLSX(D,W,X) gives the WTLS misfit M and the WTLS
% approximation DH of the data D by the model
www.eeworm.com/read/410134/11301045
m mgtlsx.m
function [m,dh] = mgtlsx(d,w,x)
% MGTLSX - Global Total Least Squares misfit computation.
% [M,DH] = MGTLSX(D,W,X) gives the GTLS misfit M and the GTLS
% approximation DH of the data D by the model B(