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m makebasis.m

function A = makebasis(X) % A = makebase(X) % % Function that creates the "faces space", i. e. the % basis of the space created throught the eigenvectors % of the covariance matrix of the populat
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m trisys.m

function X=trisys (A,D,C,B) %Input - A is the sub diagonal of the coefficient matrix % - D is the main diagonal of the coefficient matrix % - C is the super diagonal of the coefficient matr
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m idd2.m

function x=idd2(Y) %IDD2 Single-level inverse discrete 2-D wavelet transform. % IDD2 performs a single-level inverse 2-D wavelet reconstruction % using Daubechies wavelet with four coefficients
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m ghmap2.m

function b=ghmap2(a) %GHMAP2 Single-level discrete 2-D multi-wavelet transform. % GHM performs a single-level 2-D multiwavelet decomposition % using GHM multiwavelet with four multi-filters %
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m dd2.m

function Y=dd2(x) %DD2 Single-level discrete 2-D wavelet transform. % DD2 performs a single-level 2-D wavelet decomposition % using Daubechies wavelet with four coefficients % % Y = DD2(X)
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inl linearequation.inl

//LinearEquation.inl 线性方程(组)求解函数(方法)定义 // Ver 1.0.0.0 // 版权所有(C) 何渝, 2002 // 最后修改: 2002.5.31 #ifndef _LINEAREQUATION_INL #define _LINEAREQUATION_INL //全选主元高斯消去法 template int L
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m conffig.m

function fh=conffig(y, t) %CONFFIG Display a confusion matrix. % % Description % CONFFIG(Y, T) displays the confusion matrix and classification % performance for the predictions mat{y} compared with
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www.eeworm.com/read/253950/12173349

m netgrad.m

function g = netgrad(w, net, x, t) %NETGRAD Evaluate network error gradient for generic optimizers % % Description % % G = NETGRAD(W, NET, X, T) takes a weight vector W and a network data % structure
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m mlphess.m

function [h, hdata] = mlphess(net, x, t, hdata) %MLPHESS Evaluate the Hessian matrix for a multi-layer perceptron network. % % Description % H = MLPHESS(NET, X, T) takes an MLP network data structure
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htm rbffwd.htm

Netlab Reference Manual rbffwd rbffwd Purpose Forward propagation through RBF network with linear outputs. Synopsis
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