利用回声状态网络建立管式聚合反应的灰箱模型  

An approach of grey-box modeling with echo state network for tubular polymerization reaction

作　　者：秦松[1] 曹柳林[1] 

机构地区：[1]北京化工大学信息科学与技术学院,北京100029

出　　处：《计算机与应用化学》2014年第9期1096-1100,共5页Computers and Applied Chemistry

基　　金：国家自然科学基金资助项目(61174128)

摘　　要：提出一种利用回声状态网络(echo state network,ESN)建立复杂分布参数系统模型的灰箱建模方法。此建模方法可以充分利用已知机理模型的结构信息和回声状态网络的逼近能力,可更好地描述和解释出系统各变量之间的因果关系,使模型的"灰箱"化程度更高。首先,根据系统方程和先验知识将初始系统特征团引入ESN储备池中,赋予网络节点实际物理意义,并以此建立结构逼近神经网络模型;然后,通过逐步回归分析方法,结合递归最小二乘算法选择最优系统特征团,并对网络结构进行优化,建立起描述系统特性关系的灰箱模型。本文以实验室规模的管式聚合反应过程作为实验对象,建立以温度分布为输出的数学模型,结果表明所提出的灰箱建模方法行之有效。An approach of grey-box modeling with Echo State Network （ESN） is developed for modeling dynamic processes with nonlinear characteristics. This method can take full advantage of the already known structural information of the mechanism model at the early stage of modeling and make better use of the approximation ability of neural networks, thus resulting in higher accuracy of grey-box modeling. By combination the prior knowledge and systematic equations into ESN state pool, structure approaching neural network （SAAN） is established based on system feature block, and it is given actual significance. Then the optimal fundamental genes were chosen through recursive least square method with stepwise regression analysis to optimize the structure of SANN, so as to get the grey-box model. Detailed process of modeling was described in modeling of tubular polymerization reaction in laboratory scale. The simulation result proves that the approach is effective.ocesses heat exchanger network synthesis by taking place.

关 键 词：灰箱建模 管式聚合反应 结构逼近神经网络 

分 类 号：TQ015.9[化学工程] TP391.9[自动化与计算机技术—计算机应用技术]