MATLAB: Neural Network program problem in classification

Hi All

I am using this code to train my network, the problem is , if I give an input that is somehow among the value of the inputs I have chosen to train , it gives the right output , but if I give something out of this range , still the output is in the same range of the targets I have given to the code :

close all, clear all, clc, plt = 0
load('input.txt')
%load input 
load ('target.txt')
%normalizing data 
input=input';
target=target';
% input =  mapstd(input);
% target =  mapstd(target);
x=input;
t=target;
% [ x, t ]  = simpleclass_dataset;
[ I   N ] = size(x)   % [ 2 1000 ]
[ O N ] = size(t)    % [ 4 1000 ]
%vec2ind Transform vectors to indices. takes an NxM matrix V and returns a 1xM vector of indices
% indicating the position of the largest element in each column of V.
trueclass = vec2ind(t);  
class1 = find(trueclass==1);
class2 = find(trueclass==2); %in my example all the largest elements are on the 2nd column
class3 = find(trueclass==3);
class4 = find(trueclass==4);
N1 = length(class1)    
N2 = length(class2)   
N3 = length(class3)    
N4 = length(class4)    
x1 =  x(:,class1);
x2 =  x(:,class2);
x3 =  x(:,class3);
x4 =  x(:,class4);
plt = plt + 1
hold on
plot(x1(1,:),x1(2,:),'ko')
plot(x2(1,:),x2(2,:),'bo')
plot(x3(1,:),x3(2,:),'ro')
plot(x4(1,:),x4(2,:),'go')
% 
% Nw = (I+1)*H+(H+1)*O;
Hub = -1+ceil( (0.7*N*O-O)/(I+O+1))  % 399
Hmax = 40   % Hmax << Hub 
dH    = 4   % Design ~10 candidate nets
Hmin  = 2   % I know 0 and 1 are too small
rng(0)   % Allows duplicating the rsults
j=0
for h=Hmin:dH:Hmax
    j = j+1
      net            = patternnet(10);
      net = init(net);  % Improving Results since we use patternet we should use init
      [ net tr y  ]  = train( net, x, t );
      assignedclass  =  vec2ind(y);
      err = assignedclass~=trueclass;
      Nerr           = sum(err);
      PctErr(j,1)    = 100*Nerr/N;
  end
  h = (Hmin:dH:Hmax)'    
  PctErr = PctErr