how to improve ANN results by reducing error through hidden layer size, through MSE, or by using while loop

iam writing source code of my program.. in which there are 3inputs and 1 outputs kindly tell me how to reduce error

jst copy the source code and run u will find errors=gsubtracr(OUTPUTS,TARGETS).....U CAN see differnce of around 1000 or more and less in some cases i want to reduce it to zero or max 10... i want to improve my training kindly help me

a=[31 9333 2000;31 9500 1500;31 9700 2300;31 9700 2320;31 9120 2230;31 9830 2420;31 9300 2900;31 9400 2500]' g=[35000;23000;3443;2343;1244;9483;4638;4739]' h=[31 9333 2000]'

inputs =(a); targets =[g];

% Create a Fitting Network hiddenLayerSize = 1; net = fitnet(hiddenLayerSize);

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

% Setup Division of Data for Training, Validation, Testing % For a list of all data division functions type: help nndivide net.divideFcn = 'dividerand'; % Divide data randomly net.divideMode = 'sample'; % Divide up every sample net.divideParam.trainRatio = 70/100; net.divideParam.valRatio = 15/100; net.divideParam.testRatio = 15/100;

% For help on training function 'trainlm' type: help trainlm % For a list of all training functions type: help nntrain net.trainFcn = 'trainlm'; % Levenberg-Marquardt % Choose a Performance Function % For a list of all performance functions type: help nnperformance net.performFcn = 'mse'; % Mean squared error

% Choose Plot Functions % For a list of all plot functions type: help nnplot net.plotFcns = {'plotperform','plottrainstate','ploterrhist', ... 'plotregression','plotconfusion' 'plotfit','plotroc'}; % Train the Network [net,tr] = train(net,inputs,targets); plottrainstate(tr)

% Test the Network outputs = net(inputs) errors = gsubtract(targets,outputs) fprintf('errors = %4.3f\t',errors); performance = perform(net,targets,outputs);

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

% View the Network view(net); sc=sim(net,h)

Answer 1

[Greg Heath]

I answered this question in the NEWSGROUP

PLEASE DO NOT POST MULTIPLE COPIES OF THE SAME QUESTION.

IT IS RUDE, NOT TO MENTION VERY CONFUSING