Out of Memory errors in MATLAB NN Toolbox and data division problems in training datasets.

Hello:

I am facing problem in running huge data set in matlab NN Toolbox- the problem is-> when i use trainlm algorithm, NN Toolbox fails to run the data and shows Out of memory error, but for other algorithms there is no memory problem. Why is this so? Moreover when i put hidden neuron more than 15 it also shows out of memory. How to solve this kind of problems?

One more thing: i put 10, 45, 45 % data division for training -validation and testing, but after running the codes i found that in the workspace it executed 25% data for training, 37% data for validation, and 37% data for testing purpose. How to resolve this issue?

Do anybody have idea how to solve this kind of problems? I will be glad to have the comments and any kind of suggestion. Thanks.

Here is the code i used for training the dataset

EX_355 = xlsread('Training Dataset.xlsx','B2:B435106');

EX_532 = xlsread('Training Dataset.xlsx','C2:C435106');

BA_355 = xlsread('Training Dataset.xlsx','D2:D435106');

BA_532 = xlsread('Training Dataset.xlsx','E2:E435106');

BA_1064 = xlsread('Training Dataset.xlsx','F2:F435106');

Reff = xlsread('Training Dataset.xlsx','G2:G435106');

Input(1,:) = EX_355;

Input(2,:) = EX_532;

Input(3,:) = BA_355;

Input(4,:) = BA_532;

Input(5,:) = BA_1064;

Target(1,:) = Reff;

net = feedforwardnet;

net = configure(net,Input,Target);

net = init(net);

inputs = Input;

targets = Target;

hiddenLayerSize = 10;

net = fitnet(hiddenLayerSize);

net.inputs{1}.processFcns = {'removeconstantrows','mapminmax'};

net.outputs{2}.processFcns = {'removeconstantrows','mapminmax'};

net.divideFcn = 'dividerand';

net.divideMode = 'sample';

net.divideParam.trainRatio = 10/100;

net.divideParam.valRatio = 45/100;

net.divideParam.testRatio = 45/100;

net.trainFcn = 'trainlm';

net.performFcn = 'mse';

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)

trainTargets = targets .* tr.trainMask{1};

valTargets = targets .* tr.valMask{1};

testTargets = targets .* tr.testMask{1};

net.trainParam.epochs;

net.trainParam.time;

net.trainParam.goal;

net.trainParam.min_grad;

net.trainParam.mu_max;

net.trainParam.max_fail;

net.trainParam.show;