ELM代码matlab.pdf

Elmtrain .m (训练代码) function [IW,B,LW,TF,TYPE] elmtrain (P,T,N,TF,TYPE) % ELMTRAIN Create and Train a Extreme Learning Machine % Syntax % [IW,B,LW,TF,TYPE] elmtrain (P,T,N,TF,TYPE) % Description % Input % P - Input Matrix of Training Set (R*Q) % T - Output Matrix of Training Set (S*Q) % N - Number of Hidden Neurons (default Q) % TF - Transfer Function: % sig for Sigmoidal function (default) % sin for Sine function % hardlim for Hardlim function % TYPE - Regression (0,default) or Classification (1) % Output % IW - Input Weight Matrix (N*R) % B - Bias Matrix (N*1) % LW - Layer Weight Matrix (N*S) % Example % Regression: % [IW,B,LW,TF,TYPE] elmtrain (P,T,20,sig,0) % Y elmtrain (P,IW,B,LW,TF,TYPE) % Classification % [IW,B,LW,TF,TYPE] elmtrain (P,T,20,sig,1) % Y elmtrain (P,IW,B,LW,TF,TYPE) % See also ELMPREDICT % Yu Lei,11-7-2010 % Copyright www . % $Revision:1.0 $ if nargin 2 error (ELM :Arguments,Not enough input arguments.); end if nargin 3 N size (P,2); end if nargin 4 TF sig; end if nargin 5 TYPE 0; end if size (P,2) ~ size (T,2) error (ELM :Arguments,The columns of P and T must be same.); end [R,Q] size (P); if TYPE 1 T ind2vec (T); end [S,Q] size (T); % Randomly Generate the Input Weight Matrix IW rand (N,R) * 2 - 1; % Randomly Generate the Bias Matrix B rand (N,1); BiasMatrix repmat (B,1,Q); % Calculate the Layer Output Matrix H tempH IW * P + BiasMatrix; switch TF case sig H 1 ./ (1 + exp (-tempH)); case sin H sin (tempH); case hardlim H hardlim (tempH); end % Calculate the Output Weight Matrix LW pinv (H) * T; Elmpredict.m （预测代码） function Y elmpredict (P,IW,B,LW,TF,TYPE) % ELMPREDICT Simulate a Extreme Learning Machine % Syntax % Y