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/170249/9813421
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/165898/10047487
m curvefit.m
Function CurveFit(aData, sTarget1, sTarget2, sTarget3)
'MATLAB regression and curve fitting macro
MLPutMatrix "data", aData
MLEvalString "y = data(:,3)"
MLEvalString "n = len
www.eeworm.com/read/416425/11029741
m demo_lbf.m
% This Matlab file demomstrates a level set method in Chunming Li et al's paper
% "Minimization of Region-Scalable Fitting Energy for Image Segmentation",
% IEEE Trans. Image Processing, vol.
www.eeworm.com/read/469416/6976451
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
www.eeworm.com/read/143706/12849873
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/140851/13059228
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
www.eeworm.com/read/138798/13212323
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
www.eeworm.com/read/111672/6154057
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/485544/6552755
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/480149/6677920
m curvefit.m
Function CurveFit(aData, sTarget1, sTarget2, sTarget3)
'MATLAB regression and curve fitting macro
MLPutMatrix "data", aData
MLEvalString "y = data(:,3)"
MLEvalString "n = len