代码搜索：Gradient

找到约 2,951 项符合「Gradient」的源代码

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m rbfpreimg2.m

function z = rbfpreimg2(varargin) % RBFPREIMG2 RBF pre-image problem by Gradient optimization. % % Synopsis: % z = rbfpreimg2(model) % z = rbfpreimg2(model,options) % % Description: % z = rbfprei
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www.eeworm.com/read/147096/12585011

m graderr.m

function graderr(finite_diff_deriv, analytic_deriv, evalstr2) %GRADERR Used to check gradient discrepancy in optimization routines. err=max(max(abs(analytic_deriv-finite_diff_deriv))); disp(sprint
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www.eeworm.com/read/101557/15826912

m graderr.m

function graderr(finite_diff_deriv, analytic_deriv, evalstr2) %GRADERR Used to check gradient discrepancy in optimization routines. err=max(max(abs(analytic_deriv-finite_diff_deriv))); disp(sprint
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www.eeworm.com/read/390840/8437975

asv nnd12ls.asv

function nnd12ls(cmd,arg1) %NND12LS Conjugate gradient lines search demonstration. % % This demonstration requires the Neural Network Toolbox. % Copyright 1994-2002 PWS Publishing Company and T
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www.eeworm.com/read/390840/8437983

m nnd12ls.m

function nnd12ls(cmd,arg1) %NND12LS Conjugate gradient lines search demonstration. % % This demonstration requires the Neural Network Toolbox. % Copyright 1994-2002 PWS Publishing Company and T
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www.eeworm.com/read/386625/8734530

m pbicgstab.m

%PBICGSTAB Preconditioned stabilized bi-conjugate gradient method. % % [X,RESIDS,ITS]=PBICGSTAB(A,B,X0,RTOL,PRTOL,MAX_IT,MAX_TIME,MAX_MFLOP) % solves the system AX = B using the precond
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www.eeworm.com/read/428849/8834699

m contents.m

% Pre-image problem for RBF kernel. % % rbfpreimg - Schoelkopf's fixed-point algorithm. % rbfpreimg2 - Gradient optimization. % rbfpreimg3 - Kwok-Tsang's algorithm. % % About: Statistical Pattern
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www.eeworm.com/read/177674/9442492

m rbfbkp.m

function g = rbfbkp(net, x, z, n2, deltas) %RBFBKP Backpropagate gradient of error function for RBF network. % % Description % G = RBFBKP(NET, X, Z, N2, DELTAS) takes a network data structure NET % to
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www.eeworm.com/read/176823/9483188

m rbfbkp.m

function g = rbfbkp(net, x, z, n2, deltas) %RBFBKP Backpropagate gradient of error function for RBF network. % % Description % G = RBFBKP(NET, X, Z, N2, DELTAS) takes a network data structure NET % to
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www.eeworm.com/read/362500/9995856

m nnpls1.m

function [n,wts,upred]=nnpls1(t,u,ttest,utest,ii,opts) %NNPLS1 Calculates a single NN-PLS factor % Routine to carry out NNPLS. A conjugate gradient optimization % subroutine is supplied. If the
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