eddens_edsa asked . 2021-07-23

Neural Network for predictions. How to improve it

_  inputs = Inputs7old';
  targets = Mnt2old';

% Create a Fitting Network
hiddenLayerSize = 10;
FIT1 = fitnet(hiddenLayerSize);
FIT1.numLayers = 3;
FIT1.layers{2}.name='Hidden2';
FIT1.layers{3}.name='Output';
FIT1.layers{2}.dimensions=10;
FIT1.layers{3}.dimensions=1;
FIT1.inputConnect = [1; 0; 0];
FIT1.layerConnect = [0 0 0;1 0 0;1 1 0];
FIT1.outputConnect = [0 0 1];

% Choose Input and Output Pre/Post-Processing Functions
% For a list of all processing functions type: help nnprocess
FIT1.inputs{1}.processFcns = {'removeconstantrows','mapminmax'};
FIT1.outputs{2}.processFcns = {'removeconstantrows','mapminmax'};

%Initilization Functions
FIT1.layers{1}.initFcn = 'initnw';
FIT1.layers{2}.initFcn = 'initnw';
FIT1.layers{3}.initFcn = 'initnw';

%Net Input Functions
FIT1.layers{1}.netInputFcn = 'netsum';
FIT1.layers{2}.netInputFcn = 'netsum';
FIT1.layers{3}.netInputFcn = 'netsum';

%Transfer functions
FIT1.layers{1}.transferFcn = 'satlin';
FIT1.layers{2}.transferFcn = 'purelin';
FIT1.layers{3}.transferFcn = 'purelin';

%Input Weights delays
FIT1.inputWeights{2,1}.delays=0;
FIT1.inputWeights{3,1}.delays=0;

%Input Weights learning
FIT1.inputWeights{1,1}.learn=1;
FIT1.inputWeights{2,1}.learn=1;
FIT1.inputWeights{3,1}.learn=1;
FIT1.inputWeights{1,1}.learnFcn='learncon';
FIT1.inputWeights{2,1}.learnFcn='learncon';
FIT1.inputWeights{3,1}.learnFcn='learncon';

%Layer Weight Functions
FIT1.layerWeights{1,2}.weightFcn='normprod';
FIT1.layerWeights{1,3}.weightFcn='normprod';
FIT1.layerWeights{2,3}.weightFcn='normprod';
;
%Layer Initialization Functions
%FIT1.layerWeights{1,2}.initFcn='initcon';
%FIT1.layerWeights{1,3}.weightFcn='normprod';
%FIT1.layerWeights{2,3}.weightFcn='normprod';

%view(FIT1)

% Setup Division of Data for Training, Validation, Testing
% For a list of all data division functions type: help nndivide
FIT1.divideFcn = 'dividerand';  % Divide data randomly
FIT1.divideMode = 'sample';  % Divide up every sample
FIT1.divideParam.trainRatio = 64/100;
FIT1.divideParam.testRatio = 16/100;
FIT1.divideParam.valRatio = 20/100;

% For help on training function 'trainlm' type: help trainlm
% For a list of all training functions type: help nntrain
FIT1.trainFcn = 'trainrp';  % Resilient Backpropagation
%FIT1.trainFcn = 'trainlm';  % Levenberg-Marquardt
%FIT1.trainFcn = 'trainscg';  % Scaled Conjugate Gradient
%FIT1.trainFcn = 'trainbr';  % Bayesian Regularization
%FIT1.trainFcn = 'traingdm';  % Gradient descent with momentum backpropagation
%FIT1.trainFcn = 'trainb';  % Batch training
%FIT1.trainFcn = 'trainbfg';  % BFGS quasi-Newton backpropagation
%FIT1.trainFcn = 'traincgb';  % Conjugate gradient backpropagation with Powell-Beale restarts
%FIT1.trainFcn = 'trainoss';  % One-step secant backpropagation
%FIT1.trainFcn = 'trainr';  % Random order incremental training with learning functions
%FIT1.trainFcn = 'trains';  % Sequential order incremental training with learning functions

%Training Parameters
FIT1.trainParam.epochs=1000;
FIT1.trainParam.time=Inf;
FIT1.trainParam.min_grad=0.00001;
FIT1.trainParam.max_fail=6;
FIT1.trainParam.delta0=0.07;
FIT1.trainParam.delta_inc=1.25;
FIT1.trainParam.delta_dec=0.5;
FIT1.trainParam.deltamax=50;

% Choose a Performance Function
% For a list of all performance functions type: help nnperformance
FIT1.performFcn = 'mse';  % Mean squared error

% Choose Plot Functions
% For a list of all plot functions type: help nnplot
FIT1.plotFcns = {'plotperform','plottrainstate','ploterrhist', ...
  'plotregression', 'plotfit'};

% Train the Network
[FIT1,tr] = train(FIT1,inputs,targets);

% Test the Network
outputs = FIT1(inputs);
%Outputs >0
for i=1:size(Mnt2old)
   if outputs(1,i) <0
       outputs(1,i)=0.001;
   end
end
errors = gsubtract(targets,outputs);
performance = perform(FIT1,targets,outputs)

Rtr=corrcoef(outputs,Mnt2old);
R2tr=Rtr(1,2)^2

for i=1:max(size(errors))
    errors2(1,i)=errors(1,i)^2;
end
RMSEtr= (mse(errors,outputs))^0.5;

MSEtr=(sum(errors2))/max(size(Mnt2old));
RMSEtr2=MSEtr^0.5;
old=max(size(Mnt2old));
Mtrain=mean(Mnt2old);
Mquadrerrtrain=RMSEtr/(max(size(Mnt2old))^(0.5));
EpctMquad=100*Mquadrerrtrain/Mtrain
Maritmerrtrain=mean(abs(errors));
EpctMaritm=100*Maritmerrtrain/Mtrain
EpctRMSE=100+RMSEtr/Mtrain
PeaksErrratioT=EpctRMSE/EpctMaritm

% Recalculate Training, Validation and Test Performance
trainTargets = targets .* tr.trainMask{1};
%valTargets = targets  .* tr.valMask{1};
%testTargets = targets  .* tr.testMask{1};
trainPerformance = perform(FIT1,trainTargets,outputs);
%valPerformance = perform(FF1,valTargets,outputs)
%testPerformance = perform(FF1,testTargets,outputs)

% View the Network
%view(FF1)

% Plots
% Uncomment these lines to enable various plots.
%figure, plotperform(tr)
%figure, plottrainstate(tr)
%figure, plotfit(net,inputs,targets)
%figure, plotregression(targets,outputs)
%figure, ploterrhist(errors)

%Validation
Outputval=FIT1(Inputs72011');
for i=1:size(Mnt22011)
   if Outputval(1,i) <0
       Outputval(1,i)=0.001;
   end

end
plot (Outputval,'r')
hold on
plot (Mnt22011,'g')
hold off
Rval = corrcoef(Outputval,Mnt22011);
R2val=Rval(1,2)^2
errval=(Outputval-Mnt22011');
for i=1:max(size(errval))
    errval2(1,i)=errval(1,i)^2;
end
RMSEval= (mse(errval,Outputval))^0.5;
MSEval=(sum(errval2))/max(size(Mnt22011));
new=max(size(Mnt22011));
Mval=mean(Mnt22011);
Mquadrerrval=RMSEval/(max(size(Mnt22011))^(0.5));
EpcvMquad=100*Mquadrerrval/Mval
Maritmerrval=mean(abs(errval));
EpcvMaritm=100*Maritmerrval/Mval
EpcvRMSE=100*RMSEval/Mval
PeaksErrratioV=EpcvRMSE/EpcvMaritm_

errors = gsubtract(Mnt2old',ytr);
R2tr=Rtr(1,2)^2

for i=1:max(size(errors))
    errors2(1,i)=errors(1,i)^2;
end
RMSEtr= (mse(errors,ytr))^0.5;
MSEtr=(sum(errors2))/max(size(Mnt2old));
old=max(size(Mnt2old));
Mtrain=mean(Mnt2old);
Mquadrerrtrain=RMSEtr*(max(size(Mnt2old))^(-0.5));
EpctRMSE=100*RMSEtr/Mtrain
EpctMquad=100*Mquadrerrtrain/Mtrain
Maritmerrt=mean(abs(errors));
EpctMaritm=100*Maritmerrt/Mtrain
PeaksErrRatioT=EpctRMSE/EpctMaritm

yval=GRNN(Inputs72011');
figure2=plot(yval,'b');
hold on
plot(Mnt22011,'g')
hold off

Rval = corrcoef(yval,Mnt22011);
R2val=Rval(1,2)^2
errval=(yval-Mnt22011');
for i=1:max(size(errval))
    errval2(1,i)=errval(1,i)^2;
end
RMSEval= (mse(errval,yval))^0.5;
MSEval=(sum(errval2))/max(size(Mnt22011));
new=max(size(Mnt22011));
Mval=mean(Mnt22011);
Merrvalquadr=RMSEval/(max(size(Mnt22011))^(0.5));
EpcvMquadr=100*Merrvalquadr/Mval;
Merrvalaritm=mean(abs(errval));
EpcvMaritm=100*Merrvalaritm/Mval
EpcvRMSE=100*RMSEval/Mval
PeaksErrRatioV=EpcvRMSE/EpcvMaritm_

neural networks , network , prediction , training , general regression