Dear All... I've been using ANN for predicting the target value by using architecture that consists of 8 input variables, one hidden layer consists of 2 neurons, and 1 output variables; (trainlm, tansig, purelin)

yn = repmat(b2,1,N) + LW*tansig( repmat(b1,1,N) + IW*xn );

So far, I could run and got the weight and bias for each layer.

Next step, I used the same network configuration (8-2-1) for other input variables and (still the same) output variables. I could run the algorithm and plot the results, yet I could not get the weight and bias for each layer. The command window said: "Error using * Inner matrix dimensions must agree."

I have 14 input variables that considered affect the performance of target variable. I would like to do some trial-errors by combining 8 input variables among them to find out which variables that give the most significant influence. For the first combination of 8 input variables, the learning algorithm can show the weight & the bias values. But for the other combination of 8 input variables, it can process & show the figure plots, yet it has error.

Can I get the explanation why I could not get the weight and bias, please? I need those for creating the empirical formula that represent the network.

PS: I use R2016a