# What is the reason of getting high MSE even with data normalization?

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Avanthi Saumyamala el 19 de Oct. de 2018
Respondida: Ameen Bassam el 24 de Jun. de 2022
Im forecasting solar radiation using 9 input parameters.I have fitted NARX neural network but obtained very high MSE values.My input data are in different ranges and consist of many outliers..Hence the mapminmax normalization or mapstd normalization does not help me in improving the performance.How can I improve the performance of the neural network?
inputSeries = tonndata(Input,false,false); targetSeries = tonndata(Target,false,false);
% Create a Nonlinear Autoregressive Network with External Input
inputDelays = 1:2; feedbackDelays = 1:2; hiddenLayerSize = 10; net = narxnet(inputDelays,feedbackDelays,hiddenLayerSize);
% Choose Input and Feedback Pre/Post-Processing Functions
net.inputs{1}.processFcns = {'removeconstantrows','mapstd'}; net.inputs{2}.processFcns = {'removeconstantrows','mapstd'};
% Prepare the Data for Training and Simulation
[inputs,inputStates,layerStates,targets] = preparets(net,inputSeries,{},targetSeries);
% Setup Division of Data for Training, Validation, Testing
net.divideMode = 'value'; % Divide up every value
net.divideParam.trainRatio = 70/100; net.divideParam.valRatio = 15/100; net.divideParam.testRatio = 15/100;
% Choose a Training Function
net.trainFcn = 'trainlm'; % Levenberg-Marquardt
% Choose a Performance Function
net.performFcn = 'mse'; % Mean squared error
% Choose Plot Functions
net.plotFcns = {'plotperform','plottrainstate','plotresponse', ... 'ploterrcorr', 'plotinerrcorr'};
net.trainParam.max_fail=6;
% Train the Network
[net,tr] = train(net,inputs,targets,inputStates,layerStates);
% Test the Network
outputs = net(inputs,inputStates,layerStates); errors = gsubtract(targets,outputs); performance = perform(net,targets,outputs)
% Recalculate Training, Validation and Test Performance
trainTargets = gmultiply(targets,tr.trainMask); valTargets = gmultiply(targets,tr.valMask); testTargets = gmultiply(targets,tr.testMask); trainPerformance = perform(net,trainTargets,outputs) valPerformance = perform(net,valTargets,outputs) testPerformance = perform(net,testTargets,outputs)
output
performance = 3.6894e+03 trainPerformance = 3.4999e+03 valPerformance = 4.1116e+03 testPerformance = 4.1513e+03 closedLoopPerformance = 3.8351e+03 earlyPredictPerformance = 3.6894e+03
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### Respuestas (1)

Ameen Bassam el 24 de Jun. de 2022
It really depends on the data preprocessing more than the NN architecture
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