diff --git a/doc/examples.rst b/doc/examples.rst
index 8664824..ed69da6 100644
--- a/doc/examples.rst
+++ b/doc/examples.rst
@@ -670,7 +670,7 @@ Then the neural network is created. Since we have a system with 3 inputs and 2 o
 
 .. code-block:: matlab
 
-	nn = [3 4 4 2];
+	nn = [3 5 5 2];
 	dIn = [0];
 	dIntern=[];
 	dOut=[1];
diff --git a/matlab/examples/example_compair.m b/matlab/examples/example_compair.m
index 83dd662..abece47 100644
--- a/matlab/examples/example_compair.m
+++ b/matlab/examples/example_compair.m
@@ -12,16 +12,16 @@
 %%
 %Create NN
 
-%create recurrent neural network with 1 input, 2 hidden layers with 
-%3 neurons each and 1 output
+%create recurrent neural network with 3 inputs, 2 hidden layers with 
+%5 neurons each and 3 outputs
 %the NN uses the input data at timestep t-1 and t-2
 %The NN has a recurrent connection with delay of 1,2 and 3 timesteps from the output
 % to the first layer (and no recurrent connection of the hidden layers)
-nn = [3 4 4 2];
+nn = [3 5 5 2];
 dIn = [0];
 dIntern=[];
 dOut=[1];
-net = CreateNN(nn,dIn,dIntern,dOut); %alternative: net = CreateNN([3,4,4,2],[0],[],[1]);
+net = CreateNN(nn,dIn,dIntern,dOut); %alternative: net = CreateNN([3,5,5,2],[0],[],[1]);
 
 %%
 %Train with LM-Algorithm
diff --git a/matlab/examples/example_narendra4.m b/matlab/examples/example_narendra4.m
index 22576f2..5f73f5e 100644
--- a/matlab/examples/example_narendra4.m
+++ b/matlab/examples/example_narendra4.m
@@ -1,4 +1,4 @@
-%%
+u%%
 %Read Example Data
 file = 'example_data.xlsx';
 num = xlsread(file,'narendra4');
@@ -17,7 +17,7 @@
 %the NN uses the input data at timestep t-1 and t-2
 %The NN has a recurrent connection with delay of 1,2 and 3 timesteps from the output
 % to the first layer (and no recurrent connection of the hidden layers)
-nn = [1 2 2 1];
+nn = [1 3 3 1];
 dIn = [1,2];
 dIntern=[];
 dOut=[1,2,3];
diff --git a/matlab/examples/example_using_P0Y0_compair.m b/matlab/examples/example_using_P0Y0_compair.m
index 954bad6..d805c7a 100644
--- a/matlab/examples/example_using_P0Y0_compair.m
+++ b/matlab/examples/example_using_P0Y0_compair.m
@@ -20,8 +20,8 @@
 %%
 %Create NN
 
-%create recurrent neural network with 1 input, 2 hidden layers with 
-%3 neurons each and 1 output
+%create recurrent neural network with 3 inputs, 2 hidden layers with 
+%5 neurons each and 2 outputs
 %the NN uses the input data at timestep t-1 and t-2
 %The NN has a recurrent connection with delay of 1,2 and 3 timesteps from the output
 % to the first layer (and no recurrent connection of the hidden layers)
@@ -29,7 +29,7 @@
 dIn = [0];
 dIntern=[];
 dOut=[1];
-net = CreateNN(nn,dIn,dIntern,dOut); %alternative: net = CreateNN([3,4,4,2],[0],[],[1]);
+net = CreateNN(nn,dIn,dIntern,dOut); %alternative: net = CreateNN([3,5,5,2],[0],[],[1]);
 
 %%
 %Train with LM-Algorithm
diff --git a/matlab/examples/example_using_P0Y0_narendra4.m b/matlab/examples/example_using_P0Y0_narendra4.m
index 4d074fc..30becbf 100644
--- a/matlab/examples/example_using_P0Y0_narendra4.m
+++ b/matlab/examples/example_using_P0Y0_narendra4.m
@@ -25,7 +25,7 @@
 %the NN uses the input data at timestep t-1 and t-2
 %The NN has a recurrent connection with delay of 1,2 and 3 timesteps from the output
 % to the first layer (and no recurrent connection of the hidden layers)
-nn = [1 2 2 1];
+nn = [1 3 3 1];
 dIn = [1,2];
 dIntern=[];
 dOut=[1,2,3];