MATLAB: Classification

guide

I want to learn how to do classification using Neural Network in Matlab Having Elliptical Basis Function , Can any one help …. Code is attached
What I know is that first I need to create NN and then make Decision Making
Function ( Elliptical Basis Function )
 tic 
    maxround = 5;
    hiddenLayerSize = [3 5 7 10 13];
    errors = zeros(maxround,1);
    trainPerformance = zeros(maxround,1);
    valPerformance = zeros(maxround,1);
    testPerformance = zeros(maxround,1);
    timedata = zeros(maxround,1);
    NoofNeurons = zeros(maxround,1);
    Accuracy = zeros(maxround,1);
    TestFold = zeros(maxround,1); 
  NoOfClasses = zeros(maxround,1); 
 NoofInstances = zeros(maxround,1); 
 SizeofInputLayer = zeros(maxround,1); 
 for i=1: maxround
    net = patternnet(hiddenLayerSize);
    net.inputs{1}.processFcns = {'removeconstantrows','mapminmax'};
    NoOfinputs  =   net.inputs
    net.outputs{2}.processFcns = {'removeconstantrows','mapminmax'};
    NoOfOutPuts  =   net.outputs
    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.trainFcn = 'trainlm';  % Levenberg-Marquardt
    net.performFcn = 'mse';  % Mean squared error
    [net,tr] = train(net,inputs,targets);
    outputs = net(inputs);
    errors = gsubtract(targets,outputs);
    performance(i) = perform(net,targets,outputs);
    trainTargets = targets .* tr.trainMask{1};
    valTargets = targets  .* tr.valMask{1};
    testTargets = targets  .* tr.testMask{1};
    trainPerformance(i) = perform(net,trainTargets,outputs);
    valPerformance(i) = perform(net,valTargets,outputs);
    testPerformance(i) = perform(net,testTargets,outputs);
    NoofNeurons(i) = hiddenLayerSize(1);
    NameofDataSet  = 'Heart';
    TestFold(i) = i;
    NoOfClasses(i) =  size(targets,1); % Number of classed to be classified 
    NoofInstances(i) = size(targets,2); % Number of Instances 
    SizeofInputLayer(i) = size(inputs,2);
 end
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