Viscosity Prediction for PET Process Based on Hybrid Neural Networks

Based on the "divide and rule" idea, hybrid neural networks (HNNs), which consisted of linear dynamic neural network and nonlinear static neural network, was used to model for complicated nonlinear systems. By using hybrid neural networks, it can reduce the degree of difficulty for training a single network, e.g. long training time and lower accuracy; and also can transfer the solution of nonlinear control strategy into solving for linear systems based on the decomposed models. An industrial polymerization process was introduced as a powerful case-study for the demonstration of potential of neural modeling. Nonlinear predicative models, based on both serial and parallel neural networks, were applied to predict the dynamic viscosity of PET. And the results indicated that both parallel and serial hybrid neural networks can model for complicated systems well