Error using ELM Too many input arguments.

please help me to fix this code:

clc;

clear;

close all;

%TRAINING PROCESS EXTREME LEARNING MACHINE

%Mengambil Data Latih Bentuk

dt_bentuk = xlsread('inputbentuk.xls');

%Menentukan Parameter dan Kelas Target Data Latih

x1=dt_bentuk(:,1:8); %Parameter

t1=dt_bentuk(:,9); %Kelas

%Menyusun Data Latih

data_training = [x1,t1];

%Menyusun Parameter ELM

NumberofInputNeurons = 8;

NumberofHiddenNeurons = 200;

% bobot diinisialisasi secara random

InputWeight = rand(NumberofHiddenNeurons,NumberofInputNeurons)*2-1;

BiasofHiddenNeurons = rand(NumberofHiddenNeurons,1);

% bobot ditetapkan di awal

load bobotawal

Elm_Type = 1;

ActivationFunction = 'sin';

% pelatihan elm

[~, ~, ~, ~, predicted_class] = ...

ELM(data_training, data_training, ...

InputWeight, BiasofHiddenNeurons, Elm_Type,...

ActivationFunction);

% menghitung akurasi pelatihan

[~,n] = find(predicted_class==kelas);

akurasi = numel(n)/jumlah_file*100;

disp(['akurasi pelatihan = ',num2str(akurasi),'%'])

% menyimpan variabel2 pelatihan

save('net','data_training','InputWeight','BiasofHiddenNeurons',...

'Elm_Type','ActivationFunction')

Answer 1

[Voss]

I assume you are using ELM.m from here:

https://www.mathworks.com/matlabcentral/fileexchange/75653-elm

Note that that function takes five inputs:

% TrainingData_File     - Filename of training data set
% TestingData_File      - Filename of testing data set
% Elm_Type              - 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)

And you have given it six inputs:

[~, ~, ~, ~, predicted_class] = ...
    ELM(data_training, data_training, ...           % 1 and 2 (both identical)
    InputWeight, BiasofHiddenNeurons, Elm_Type,...  % 3, 4 and 5
    ActivationFunction);                            % 6

Check the inputs you are giving ELM.m.