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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 mdnpak.m

function w = mdnpak(net) %MDNPAK Combines weights and biases into one weights vector. % % Description % W = MDNPAK(NET) takes a mixture density network data structure NET % and combines the network w
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m demolgd1.m

%DEMOLGD1 Demonstrate simple MLP optimisation with on-line gradient descent % % Description % The problem consists of one input variable X and one target variable % T with data generated by sampling X
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m nethess.m

function [h, varargout] = nethess(w, net, x, t, varargin) %NETHESS Evaluate network Hessian % % Description % % H = NETHESS(W, NET, X, T) takes a weight vector W and a network data % structure NET, to
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m mdn.m

function net = mdn(nin, nhidden, ncentres, dim_target, mix_type, ... prior, beta) %MDN Creates a Mixture Density Network with specified architecture. % % Description % NET = MDN(NIN, NHIDDEN, NCENTRE
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m rbfpak.m

function w = rbfpak(net) %RBFPAK Combines all the parameters in an RBF network into one weights vector. % % Description % W = RBFPAK(NET) takes a network data structure NET and combines the % componen
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htm mdnpak.htm

Netlab Reference Manual mdnpak mdnpak Purpose Combines weights and biases into one weights vector. Synopsis w =
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