代码搜索：Gradient

找到约 2,951 项符合「Gradient」的源代码

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m nnd12cg.m

function nnd12cg(cmd,arg1) %NND12CG Conjugate gradient backpropagation demonstration. % % This demonstration requires the Neural Network Toolbox. % First Version, 8-31-95. %==================
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www.eeworm.com/read/161189/10440092

m bfgs.m

% % xstar=bfgs(func,delf,x0,tol,maxiter) % % Use steepest descent to minimize a function f(x). % % func name of the function f(x) % delf name of the gradient del f(x) % x0
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www.eeworm.com/read/351010/10688422

m nnd12cg.m

function nnd12cg(cmd,arg1) %NND12CG Conjugate gradient backpropagation demonstration. % % This demonstration requires the Neural Network Toolbox. % First Version, 8-31-95. %==================
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www.eeworm.com/read/158963/10707516

m nnd12cg.m

function nnd12cg(cmd,arg1) %NND12CG Conjugate gradient backpropagation demonstration. % % This demonstration requires the Neural Network Toolbox. % First Version, 8-31-95. %==================
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www.eeworm.com/read/274975/10841965

m nnd12cg.m

function nnd12cg(cmd,arg1) %NND12CG Conjugate gradient backpropagation demonstration. % % This demonstration requires the Neural Network Toolbox. % First Version, 8-31-95. %==================
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www.eeworm.com/read/448535/7531583

m conjgradtest.m

% Test the conjugate gradient algorithm % Copyright 1999 by Todd K. Moon hold off ; rosenbrock; hold on xoff = -.3; x = [-1;-1]; [xn,X] = conjgrad2(x,'rosengrad','rosenhess') [n,k] = size
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www.eeworm.com/read/447486/7550068

m definemodel.m

function [model] = defineModel(dx,dy,model,quantVal) %Build a 2D Gradient histogram %Scale image between 0 and 255 sgradX = scale(dx,[0 255]); sgradY = scale(dy,[0 255]); %Quantize image
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www.eeworm.com/read/434325/7874550

m grads.m

function [P0,y0,h,err,P,Y] = grads(Fn,Gn,P0,max1,delta,epsilon,show) %--------------------------------------------------------------------------- %GRADS Gradient search for a minimum. % Sample ca
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www.eeworm.com/read/298871/7928892

m nnd12cg.m

function nnd12cg(cmd,arg1) %NND12CG Conjugate gradient backpropagation demonstration. % % This demonstration requires the Neural Network Toolbox. % First Version, 8-31-95. %==================
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www.eeworm.com/read/396828/8088287

m traincgp_snn.m

function [net, result] = traincgp_snn(net, dataLV, dataVV, dataTV) %TRAINCGP_SNN Conjugate gradient training (Polak - Ribiere). % % Syntax % % [net, tr_info] = traincgp_snn(net, dataLV) % [net, t
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