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NNregression2algorithms.m
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clear;
load TTE
Nav=1;
No_Points=20; Multi=1;
EEE=zeros(No_Points,No_Points);
for i=1:1:No_Points
for j=1:1:No_Points
for k=1:Nav
clear net
hiddenLayerSize1 =Multi * i;
hiddenLayerSize2 =Multi * j;
net = newff(minmax(inputs),[hiddenLayerSize1 hiddenLayerSize2 1],{'logsig' 'logsig' 'purelin'},'trainrp');
net.performFcn = 'mse'; % Mean squared error
net.inputs{1}.processFcns = {'removeconstantrows','mapminmax'};
net.outputs{2}.processFcns = {'removeconstantrows','mapminmax'};
net.divideFcn = 'divideind';
net.divideParam.trainInd = trainInd;
net.divideParam.valInd = valInd;
net.divideParam.testInd = testInd;
net.divideMode = 'sample';
net.trainParam.goal=0;
net.trainParam.epochs=100;
net.trainParam.max_fail=50;
net.trainParam.showWindow=0;
[net,tr] = train(net,inputs,targets);
%net.performFcn='msereg'; net.performParam.ratio=0.5;
net.plotFcns = {'plotperform','plottrainstate','ploterrhist', ...
'plotregression', 'plotfit'};
[net,tr] = train(net,inputs,targets);
outputs = net(inputs);
errors = gsubtract(targets,outputs);
performance = perform(net,targets,outputs);
EEE(i,j)=EEE(i,j)+ performance/Nav;
EEErp(i,j)=EEE(i,j);
'1-RP'
((i-1)*No_Points+j)/No_Points/No_Points*100
end
end
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
EEE=zeros(No_Points,No_Points);
%20 trials of hidden layer
for i=1:1:No_Points
for j=1:1:No_Points
for k=1:Nav
clear net
hiddenLayerSize1 =Multi* i;
hiddenLayerSize2 =Multi *j;
net = newff(minmax(inputs),[hiddenLayerSize1 hiddenLayerSize2 1],{'logsig' 'logsig' 'purelin'},'trainlm');
net.performFcn = 'mse'; % Mean squared error
net.inputs{1}.processFcns = {'removeconstantrows','mapminmax'};
net.outputs{2}.processFcns = {'removeconstantrows','mapminmax'};
net.divideFcn = 'divideind';
net.divideParam.trainInd = trainInd;
net.divideParam.valInd = valInd;
net.divideParam.testInd = testInd;
net.divideMode = 'sample';
net.divideParam.trainRatio = 70/100;
net.divideParam.valRatio = 15/100; %40/100;
net.divideParam.testRatio = 15/100;
net.trainParam.goal=0;
net.trainParam.epochs=100;
net.trainParam.max_fail=50;
net.trainParam.showWindow=0;
[net,tr] = train(net,inputs,targets);
%net.performFcn='msereg'; net.performParam.ratio=0.5;
net.plotFcns = {'plotperform','plottrainstate','ploterrhist', ...
'plotregression', 'plotfit'};
[net,tr] = train(net,inputs,targets);
outputs = net(inputs);
errors = gsubtract(targets,outputs);
performance = perform(net,targets,outputs);
EEE(i,j)=EEE(i,j)+ performance/Nav;
EEElm(i,j)=EEE(i,j);
'2-LM'
((i-1)*No_Points+j)/No_Points/No_Points*100
end
end
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
EEE=zeros(No_Points,No_Points);
%20 trials of hidden layer
for i=1:1:No_Points
for j=1:1:No_Points
for k=1:Nav
clear net
hiddenLayerSize1 =Multi* i;
hiddenLayerSize2 =Multi *j;
net = newff(minmax(inputs),[hiddenLayerSize1 hiddenLayerSize2 1],{'logsig' 'logsig' 'purelin'},'trainscg');
net.performFcn = 'mse'; % Mean squared error
net.inputs{1}.processFcns = {'removeconstantrows','mapminmax'};
net.outputs{2}.processFcns = {'removeconstantrows','mapminmax'};
net.divideFcn = 'divideind';
net.divideParam.trainInd = trainInd;
net.divideParam.valInd = valInd;
net.divideParam.testInd = testInd;
net.divideMode = 'sample';
net.divideParam.trainRatio = 70/100;
net.divideParam.valRatio = 15/100; %40/100;
net.divideParam.testRatio = 15/100;
net.trainParam.goal=0;
net.trainParam.epochs=100;
net.trainParam.max_fail=50;
net.trainParam.showWindow=0;
[net,tr] = train(net,inputs,targets);
%net.performFcn='msereg'; net.performParam.ratio=0.5;
net.plotFcns = {'plotperform','plottrainstate','ploterrhist', ...
'plotregression', 'plotfit'};
[net,tr] = train(net,inputs,targets);
outputs = net(inputs);
errors = gsubtract(targets,outputs);
performance = perform(net,targets,outputs);
EEE(i,j)=EEE(i,j)+ performance/Nav;
EEEscg(i,j)=EEE(i,j);
'3-SCG'
((i-1)*No_Points+j)/No_Points/No_Points*100
end
end
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
EEE=zeros(No_Points,No_Points);
%20 trials of hidden layer
for i=1:1:No_Points
for j=1:1:No_Points
for k=1:Nav
clear net
hiddenLayerSize1 =Multi* i;
hiddenLayerSize2 =Multi *j;
net = newff(minmax(inputs),[hiddenLayerSize1 hiddenLayerSize2 1],{'logsig' 'logsig' 'purelin'},'trainbr');
net.performFcn = 'mse'; % Mean squared error
net.inputs{1}.processFcns = {'removeconstantrows','mapminmax'};
net.outputs{2}.processFcns = {'removeconstantrows','mapminmax'};
net.divideFcn = 'divideind';
net.divideParam.trainInd = trainInd;
net.divideParam.valInd = valInd;
net.divideParam.testInd = testInd;
net.divideMode = 'sample';
net.divideParam.trainRatio = 70/100;
net.divideParam.valRatio = 15/100; %40/100;
net.divideParam.testRatio = 15/100;
net.trainParam.goal=0;
net.trainParam.epochs=100;
net.trainParam.max_fail=50;
net.trainParam.showWindow=0;
[net,tr] = train(net,inputs,targets);
%net.performFcn='msereg'; net.performParam.ratio=0.5;
net.plotFcns = {'plotperform','plottrainstate','ploterrhist', ...
'plotregression', 'plotfit'};
[net,tr] = train(net,inputs,targets);
outputs = net(inputs);
errors = gsubtract(targets,outputs);
performance = perform(net,targets,outputs);
EEE(i,j)=EEE(i,j)+ performance/Nav;
EEEbr(i,j)=EEE(i,j);
'4-BR'
((i-1)*No_Points+j)/No_Points/No_Points*100
end
end
end
subplot(2,2,1);
title('BBBB')
%No_Points=10;
x = [1 No_Points];
y = [1 No_Points];
clims = [5 50]; % TTE 5-50 DWFD 0 2500 DWFU 100 2700 swcd 0.0003 0.006 swcu 0.0003 0.01
imagesc(x,y,EEElm,clims)
set(gca,'YDir','normal')
colorbar
title('LM')
xlabel('No. of Hidden Units 2nd Layer','FontSize',10)
ylabel('No. of Hidden Units 1st Layer','FontSize',10)
subplot(2,2,2);
imagesc(x,y,EEErp,clims)
set(gca,'YDir','normal')
colorbar
title('RP')
xlabel('No. of Hidden Units 2nd Layer','FontSize',10)
ylabel('No. of Hidden Units 1st Layer','FontSize',10)
subplot(2,2,3);
imagesc(x,y,EEEbr,clims)
set(gca,'YDir','normal')
colorbar
title('BR')
xlabel('No. of Hidden Units 2nd Layer','FontSize',10)
ylabel('No. of Hidden Units 1st Layer','FontSize',10)
subplot(2,2,4);
imagesc(x,y,EEEscg,clims)
set(gca,'YDir','normal')
colorbar
title('SCG')
xlabel('No. of Hidden Units 2nd Layer','FontSize',10)
ylabel('No. of Hidden Units 1st Layer','FontSize',10)