代码搜索：Regularized
找到约 102 项符合「Regularized」的源代码
代码结果 102
www.eeworm.com/read/139772/13135441
mes~orig forces49.mes~orig
step=0
startvalue corrected in order to fit bounds
donlp2, v3, 05/29/98, copyright P. Spellucci
Thu Feb 24 16:55:06 2000
forces49
step=1
rankdeficiency of grad's of active constr.!
del= 2.00000000000
www.eeworm.com/read/307647/13718068
m dirac.m
function y = dirac( x, epsilon )
% DIRAC Dirac function of x
% DIRAC( x, epsilon ) Computes the derivative of the heaviside
% function of x with respect to x. Regularized based on epsilon.
www.eeworm.com/read/307647/13718076
m heaviside.m
function value = heaviside( z, epsilon )
% HEAVISIDE Heaviside function of z
% HEAVISIDE( z, epsilon ) Heaviside function of z, regularized
% based on epsilon.
value = 0.5 .* ( 1 + ( 2 ./
www.eeworm.com/read/226683/14455533
m dirac.m
function y = dirac( x, epsilon )
% DIRAC Dirac function of x
% DIRAC( x, epsilon ) Computes the derivative of the heaviside
% function of x with respect to x. Regularized based on epsilon.
y =
www.eeworm.com/read/226683/14455557
m heaviside.m
function value = heaviside( z, epsilon )
% HEAVISIDE Heaviside function of z
% HEAVISIDE( z, epsilon ) Heaviside function of z, regularized
% based on epsilon.
value = 0.5 .* ( 1 + ( 2 ./ pi )
www.eeworm.com/read/190387/8444270
m rbf.m
function w = rbf(x, t, d, sigma, lam)
% function w = rbf(x,t,d,sigma,lam)
%
% Determines weights for a regularized radial basis function network.
%
% x - data
% t - centers
% d - de
www.eeworm.com/read/168045/9941054
m rbf.m
function w = rbf(x, t, d, sigma, lam)
% function w = rbf(x,t,d,sigma,lam)
%
% Determines weights for a regularized radial basis function network.
%
% x - data
% t - centers
% d - de
www.eeworm.com/read/343753/6963588
m traintr.m
function [a,b,c,d,e,f,g,h] = traintr(i,j,k,l,m,n,o,p,q,r,s,t,u,v,x,y,z)
%TRAINTR trains a feed-forward network with 2 or 3 hidden layers
%using the Gauss-Newton method on a Tikhonov regularized proble
www.eeworm.com/read/343753/6963589
m trainltr.m
function [a,b,c,d,e,f,g,h] = trainltr(i,j,k,l,m,n,o,p,q,r,s,t,u,v,x,y,z)
%
%TRAINLTR trains a large feed-forward network with 2 or 3 hidden layers
%using a truncated Gauss-Newton method on a Tikhonov
www.eeworm.com/read/343753/6963600
m ttr3.m
function [w1,b1,w2,b2,w3,b3,tr,rq] = ttr3(w1,b1,f1,w2,b2,f2,w3,b3,f3,...
xc,P,T,VA,VAT,TE,TET,TP)
%TTR3 Trains a feed-forward network with one hidden layer
%using the Gauss-Newton method on a Ti