Melt index prediction of polypropylene is important but very difficult.A method based on PCA-RBF neural network which is optimized by a new ant colony system is proposed.Principal component analysis(PCA)is used to map high-dimension initial data to new low-dimension data

and then the corrections of the input variables are eliminated and the most relevant process features are selected.Radical basis function(RBF)neural network is used to characterize the nonlinearity.Finally the new ACS which works for continuous optimization problems is employed to optimize the weights of the RBF neural network.According to the research on the data from a real plant

it shows that the model works well and provides promising accuracy and reliability.