Using Parallel computing in training neural networks

I am trying to use parallel computing toolbox for training a neural network in MATLAB on a laptop which has a core-iV cpu and MATLAB 2015a. It keeps giving me an error. I am following exactly the instructions provided by mathworks. Even the sample codes from mathworks are not working and giving me the same error. Here is my code:

clc;clear;

delete(gcp);

pool=parpool ;

x=(2*rand(1,1000)-ones(1,1000));

t=zeros(1,1000); %optional output (Do not worry about being zero, it is just a sample code for my error)

net.layers{1}.transferFcn = 'tansig';

net.layers{2}.transferFcn = 'purelin';

trainFcn = 'trainbr';

hiddenLayerSize = 30;

net = fitnet(hiddenLayerSize,trainFcn);

net.trainParam.epochs= 40000;

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;

net.performFcn = 'mse'; % Mean Squared Error

% Train the Network

[net,tr] = train(net,x,t,'useParallel','yes');

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And here is the error message.

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Error using Composite/subsasgn (line 96)

An invalid indexing request was made.

Error in nncalc.setup1>setupImpl (line 453)

    calcData{i} = datai;

Error in nncalc.setup1 (line 17)

[calcMode,calcNet,calcData,calcHints,net,resourceText] = setupImpl(calcMode,net,data);

Error in nncalc.setup (line 7)

[calcMode,calcNet,calcData,calcHints,net,resourceText] = nncalc.setup1(calcMode,net,data);

Error in network/train (line 357)

    [calcLib,calcNet,net,resourceText] = nncalc.setup(calcMode,net,data);

Error in worked_with_parallerl_computing (line 27)

[net,tr] = train(net,x,t,'useParallel','yes');

Caused by:

    Error using Composite/errorIfRemoteOperationInProgress (line 170)
    The Composite value cannot be accessed because
    a remote operation is in progress.

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Any help would be appreciated. BTW, the code copied above is just a sample code for illustrating the problem (I know I do not need parallel processing for training such a network).