Abstract: 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.

摘要： 提出反映炼油厂分馏装置动态特性的前馈神经网络模型 ,根据工厂的生产实际及数据特点建立了一种基于时间序列的、适合油品质量指标监测的动态系统前馈神经网络 (DBPNN)结构 .通过用实验室模拟的动态过程数据和炼油厂分馏装置的生产数据分别建模并与传统静态前馈神经网络模型比较 ,结果表明 ,DBPNN模型能够反映动态过程的特性 ,并具有更高的可靠性和适应性 .