代码搜索：NetWork

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m nncallbk.m

function y = nncallbk(demo,command) %NNCALLBK Neural Network Design utility function. % First Version, 8-31-95. % NNCALLBK(DEMO,COMMAND) % DEMO - Name of demo. % COMMAND - Command. % Ret
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m nnpause.m

function nnpause(delay) %NNPAUSE A Neural Network Design utility function. % First Version, 8-31-95. %================================================================== drawnow start = cloc
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m contents.m

% Neural Network Design Demonstrations. % Copyright (c) 1994 by PWS Publishing Company. % % General % nnd - Splash screen. % nndtoc - Table of contents. % nnsound - Turn Neural Net
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m nndrwcir.m

function nndrwcir(x,y,r,c) %NNDRWCIR Neural Network Design utility function. % % NNDRWCIR(X,Y,R,C) % X - Horizontal coordinate. % Y - Vertical coordinate. % R - Radius. % C - Color. %
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m nnfexist.m

function ok = nnfexist(d) %NNFEXIST Neural Network Design utility function. % First Version, 8-31-95. %================================================================== ok = exist('hardlim'
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m contents.m

% MININNET % Functions for Neural Network Design demonstrations. % (Do not use if Neural Network Toolbox is available.) % % Transfer functions % compet - Competitive transfer function. % h
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m train.m

function net = train(net, tutor, varargin) % TRAIN % % Train a max-win multi-class support vector classifier network using the % specified tutor to train each component two-class network. %
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m train.m

function net = train(net, tutor, varargin) % TRAIN % % Train a max-win multi-class support vector classifier network using the % specified tutor to train each component two-class network. %
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m train.m

function net = train(net, tutor, varargin) % TRAIN % % Train a dag-svm multi-class support vector classifier network using the % specified tutor to train each component two-class network. %
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m train.m

function net = train(net, tutor, varargin) % TRAIN % % Train a max-win multi-class support vector classifier network using the % specified tutor to train each component two-class network. %
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