diff --git a/matlab/examples/loadNN.m b/matlab/examples/loadNN.m
deleted file mode 100644
index bc7f966..0000000
--- a/matlab/examples/loadNN.m
+++ /dev/null
@@ -1,64 +0,0 @@
-function net = loadNN(filename)
-    %Load neural network object from file
-    % 		
-    % 	Args:
-    % 		filename:	path to csv file to save neural network
-    %
-    %   Returns:
-    %       net:        neural network object
-
-    [~,~,rawData] = xlsread(filename);
-
-    %read neural network structure nn
-    nn = str2num(rawData{2,1});
-
-    %read input delays dIn
-    if isnumeric(rawData{4,1})
-       dIn = rawData{4,1};
-    else
-       dIn = str2num(rawData{4,1});
-    end
-    
-    %read iternal delays dIntern
-    if isnumeric(rawData{6,1})
-        dIntern = rawData{6,1};
-    elseif rawData{6,1}==','
-        dIntern = []; 
-    else
-        dIntern = str2num(rawData{6,1});
-    end
-   
-    %read output delays dOut
-    if isnumeric(rawData{8,1})
-        dOut = rawData{8,1};
-    elseif rawData{8,1}==','
-        dOut = []; 
-    else
-        dOut = str2num(rawData{8,1});
-    end
-    
-    %read factor for input data normalization normP
-    if isnumeric(rawData{10,1})
-       normP = rawData{10,1};
-    else
-       normP = str2num(rawData{10,1})';
-    end
-    
-    %read factor for output data normalization normY
-    if isnumeric(rawData{12,1})
-       normY = rawData{12,1};
-    else
-       normY = str2num(rawData{12,1})';
-    end
-    
-    %read weight vector w
-    w = csvread(filename,13,0);
-    
-    %Create neural network and assign loaded weights and factors
-    net = CreateNN(nn,dIn,dIntern,dOut);
-    net.normP = normP;
-    net.normY = normY;
-    net.w = w;
-    
-    
-end
\ No newline at end of file