www.pudn.com > ELM.rar > DEMO.m, change:2013-07-21,size:965b


clc 
clear 
 
 
 
TrainingData_File=load('sinc_train.mat'); 
TestingData_File=load('sinc_test.mat'); 
Elm_Type=0; 
NumberofHiddenNeurons=20; 
ActivationFunction='sig'; 
 
% Elm_Type              - ELM as functional approximators or classifiers;0 for regression; 1 for (both binary and multi-classes) classification 
% NumberofHiddenNeurons - Number of hidden neurons assigned to the ELM 
% ActivationFunction    - Type of activation function: 
%                           'sig' for Sigmoidal function 
%                           'sin' for Sine function 
%                           'hardlim' for Hardlim function 
%                           'tribas' for Triangular basis function 
%                           'radbas' for Radial basis function (for additive type of SLFNs instead of RBF type of SLFNs) 
 
[TrainingTime TestingTime TrainingAccuracy TestingAccuracy] = ELM(TrainingData_File, TestingData_File, Elm_Type, NumberofHiddenNeurons, ActivationFunction);