The artificial neural networks are now being widely studied and used in many fields because of their high capability of mapping the input-output relationship of nonlinear systems.However

for nonlinear dynamic systems

such as the fractionating equipment of refinery

the standard back-propagation(BP) neural network——which is the most often studied and used

can not perform very well when used to model these systems

especially when they are not in steady states.So

in this paper

a new structure of feed forward neural network named dynamic back propagation neural network(DBPNN)

based on back propagation algorithm and time series model is presented.The DBPNN can be used to model dynamic nonlinear systems.Furthermore

with the special structure of the integrated node layer

the complexity of the DBPNN is largely reduced.By using data from laboratory model and data from the fractionating equipment of some refinery

DBPNN models are built.The result shows that the DBPNN models can identify the dynamic behavior of nonlinear dynamic systems and are more accurate and reliable

compared with the result of models of these two nonlinear dynamic systems using standard back propagation neural networks.