一种用于动态化工过程建模的反馈神经网络新结构

提出了一种新的用于非线性动态化工过程的状态集成反馈神经网络结构 (SIRNN) ,并将静态BP网络的训练算法引入到该网络的训练中 .状态反馈、时间序列延迟与集成节点的概念结合在SIRNN结构中 ,使得在用SIRNN建模过程中既可以考虑系统过去更多时刻的状态信息又可以相对降低网络的复杂程度 ,使得网络结构更趋于合理 .将SIRNN对一单输入单输出二阶非线性动态系统建模 ,并与其他反馈神经网络建模效果进行了比较 ,同时对该网络结构进行了抗干扰性检验 ,并对其在多输入单输出系统的应用中进行了尝试 ,结果表明SIRNN结构对非线性动态系统建模具有快速、高效和抗干扰的良好性能

NEW KIND OF RECURRENT NEURAL NETWORKS USED IN DYNAMIC CHEMICAL PROCESS

A new kind of recurrent neural network structure-state integrated recurrent neural network (SIRNN), which can be used in modeling nonlinear dynamic chemical process systems, is put forward here and the BP algorithm,used in training multi-layer feed-forward neural networks, is used as its learning algorithm. The special recurrent structure of SIRNN, which successfully combines state feed-back, time-series and integrated node together, is different from other recurrent neural networks that it makes the network memorize more past system states and that it keeps the network structure from being too complex. Thereby, SIRNN can be used in modeling high order nonlinear dynamic systems. Input-output models of a second order dynamic nonlinear SISO system using different recurrent neural networks are respectively built and their performance is compared. The result shows that the model using SIRNN performs better than other three recurrent neural networks and also, the SIRNN has higher ability of fault tolerance. This indicates its promising future in the application to true systems. The SIRNN is also used in modeling a MISO CSTR system and its performance is tested. The result shows that after training, the model output can well accord with the output trend of the CSTR system.