代码搜索：Fitting
找到约 695 项符合「Fitting」的源代码
代码结果 695
www.eeworm.com/read/479088/6699286
m wls.m
function polyestim = wls(x,y,w,order)
%
% Calculates polynomial estimates using weighted least squares fitting.
% x: Time coordinates of the measurements.
% y: Instananeous range measurements.
%
www.eeworm.com/read/251522/4418954
m maximize_params.m
function CPD = maximize_params(CPD)
% MAXIMIZE_PARAMS Set the params of a CPD to their ML values (Gaussian)
% CPD = maximize_params(CPD)
% For details, see "Fitting a Conditional Gaussian Distributi
www.eeworm.com/read/188237/5209751
err project.err
PSD Fitter - Logic Synthesis and Device Fitting
PSDsoft Express 8.30 Copyright (C) 1993-2004 STMicroelectronics, Inc. All Rights Reserved.
PROJECT : project DATE : 10/08
www.eeworm.com/read/293336/3931291
m example_script_spec2param0_fit.m
% Example Script for MS_AR_Fit_param0.m (run it in the same directory)
% this example will pass an personalized initial parameter vector for the
% fitting function.
% OBS: So far, the routines w
www.eeworm.com/read/396844/2406970
m maximize_params.m
function CPD = maximize_params(CPD)
% MAXIMIZE_PARAMS Set the params of a CPD to their ML values (Gaussian)
% CPD = maximize_params(CPD)
% For details, see "Fitting a Conditional Gaussian Distributi
www.eeworm.com/read/379369/9199482
txt 2dgaussanfitting.txt
Matlab 2D Gaussian fitting code
To use this code, you can mark the text below with the mouse and copy and paste it via the windows clipboard into a Matlab M-file editor window.
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www.eeworm.com/read/374010/9423745
m test1.m
% Demonstration of different neural network training algorithms used
% for curve fitting
close all
%---------- Generate training and test set ----------
clc
PHI=0:0.25:6;
Y=sin(PHI);
PHI1 = P
www.eeworm.com/read/177674/9442588
m demmdn1.m
%DEMMDN1 Demonstrate fitting a multi-valued function using a Mixture Density Network.
%
% Description
% The problem consists of one input variable X and one target variable
% T with data generated by
www.eeworm.com/read/176823/9483267
m demmdn1.m
%DEMMDN1 Demonstrate fitting a multi-valued function using a Mixture Density Network.
%
% Description
% The problem consists of one input variable X and one target variable
% T with data generated by