How to apply an divideFcn 'dividedblock' by a Neural Network?

Hello,

I want try to predict the flow of air in a system. Therefore I take 15 steps in the past to predict 4 steps ahead. For avoiding overfitting I will stop the training when the performance of the validationset isn't consequent with the trainset performance. Normally the dividedFcn divided the input en targets in a train, validation and test set. When I apply this function there is an error. Is somebody know how to solve this? Later I want to extend the model with other inputs, there for I want to work with Cells.

For experimenting there is a script with a know function. In this script are al the 'features' implemented en tested. But the divideFcn doesn't work.

 clear all
 close all
 clc

 % parameters model 
 Input_Delays = 15;
 Targets = 4;
 Numeber_Neurons = 20;
 epochs=100;
 trainFcn = 'trainlm';

 %%make train & validationi data 
 t_Train = 1:200;
 x_Train = 10.*sin(t_Train)+t_Train.*t_Train./100;

 %%independent test data  
 t_Test  = 201:300;
 x_Test  = 10.*sin(t_Test )+t_Test .*t_Test ./100;

 %%Prepare Train & Validation DATA 
 for i = 1:length(x_Train)-Input_Delays-Targets
     % load prepariation 
     X_Seq_Input_Train{:,i}  = x_Train(i:i+Input_Delays-1)';
     T_Seq_Target_Train{:,i} = 
 x_Train(i+Input_Delays:i+Input_Delays+Targets-1)';
     Time_Targets_Train{:,i} = 
 t_Train(i+Input_Delays:i+Input_Delays+Targets-1)';
 end

 %%Prepaere Test DATA 
 for i = 1:length(x_Test)-Input_Delays-Targets
     % load prepariation 
     X_Seq_Input_Test{:,i}  = x_Test(i:i+Input_Delays-1)';
     T_Seq_Target_Test{:,i}= 
 x_Test(i+Input_Delays:i+Input_Delays+Targets-1)';
     Time_Targets_Test{:,i} = 
 t_Test(i+Input_Delays:i+Input_Delays+Targets-1)';
  end

 % make forwardnet 
 net= feedforwardnet(Numeber_Neurons);  

 net.Inputs{1}.name= 'Timestamps';       % name of input 
 net.Inputs{1}.size =Input_Delays ;      % number of inputs 
 net.Layers{1}.size = Numeber_Neurons;   % Number of neurons 

 % split Dataset Train

Here is the problem

 net.divideFcn             ='divideblock'  ;
 net.divideParam.trainRatio=0.8;
 net.divideParam.valRatio  =0.2;
 net.divideParam.testRatio =0.0;

 % %tempory setting but doensn't effect the training process 
 % net.divideFcn = 'dividerand';  
 % net.divideMode = 'sample';  
 % net.divideParam.trainRatio = 70/100;
 % net.divideParam.valRatio = 15/100;
 % net.divideParam.testRatio = 15/100;
 % net.trainParam.showWindow = false;

 net.trainFcn          = trainFcn;
 net.trainParam.epochs = epochs;
 %net.trainParam.showWindow = false;
 %%train 
 [net,tr] = train(net,X_Seq_Input_Train,T_Seq_Target_Train,'useParallel','no');

 %%Simulate on train 
 [A_Train] = sim(net,X_Seq_Input_Train);