Modeling of a chemical reactor with serial/parallel neural networks

Modeling studies of complex nonlinear systems by hybrid neural networks are described in this paper.The hybrid neural networks consist of linear dynamic neural networks and nonlinear static neural networks.Using such a hybrid neural network,the difficulties in training a single network can be decreased,and the solution for a nonlinear control strategy can be reduced to solving for a linear system.Thus,this method overcomes the shortcomings of long training time and lower accuracy observed for one single neural network.Both the serial and parallel neural networks have been used to model a CSTR.A comparison between the two hybrid neural networks on the basis of the results obtained is described.