The molecular weight distribution (MWD) of polymer is one of the most important performance indexes

which is a kind of typical binary modeling. A method based on hybrid discrete orthogonal polynomial neural network (DOPNN) was proposed to model the MWD of polymers. First

the space and time variables were decomposed by the hybrid neural network. The DOPNN was used to obtain the space model of MWD

and the relationship between MWD and input variables (namely the time variables) was converted into the function between weight vector of space model and input variables. Second

the recurrent neural network was used to obtain the time model. Last

the modeling destination was reached by combining the two NN models mentioned above. The mathematical expression of the model was similar with the traditional discrete state-space expression. Based on the model

an easy way to design the control strategy could be achieved. In space modeling

the weight vector of NN was equivalent to the moment of MWD. That is to say

the weight vector of neural network was of practical significance

so that the grey box model could be obtained. A solution to forecast the number of hidden nodes of neural network was provided. The experimental system investigated was the styrene polymerization in CSTR. The results of the experiment indicated that the NN model was able to capture the MWD as well as to provide accurate moment of MWD through the weight vector of NN model.