input = rand(100,1);
inputG = linspace(0,1,50)';
output = (1+sin(4*pi*input-pi/8)) .* (0.1./(0.1+input));
outputG = (1+sin(4*pi*inputG-pi/8)) .* (0.1./(0.1+inputG));
outputG = outputG + 0.01*randn(size(outputG))*(max(outputG)-min(outputG));
dataVal = [inputG outputG];
dataTest = [inputG outputG];
output = output + 0.05*randn(size(output))*(max(output)-min(output));
dataTrain = [input output];
[LMNBest AllLMN] = LMNTrain(dataTrain, dataVal, dataTest);
figure
outputModelG = LMNBest.calculateModelOutput(inputG);
hold on
plot(inputG,outputG,'color',[0.9 0.9 0.9],'linestyle','-','linewidth',1.5)
plot(inputG,outputModelG,'k-')
plot(input,output,'kx','markersize',12)
hold off
box on
axis([0 1 -0.2 1])
xlabel('input')
ylabel('output')
legend('process','model','data')
Current training method: lolimotQuad
Current training method: lolimotSparseQuad
Current training method: lolimot
Current training method: hilomot
Current training method: hilomotQuad
Current training method: hilomotSparseQuad
Suggested model: lolimotSparseQuad
Warning: Ignoring extra legend entries.