摘要
提出一种基于反应基元的建立复杂非线性系统模型的灰箱建模方法。首先根据先验知识及系统特性分析引入过程的初始反应基元,并以此为出发点建立结构逼近神经网络模型,实现基元之间的关联,赋予网络节点实际的物理意义;然后,通过提出的最小化预测误差,结合逐步回归分析方法选择最优反应基元,优化网络结构,建立起表示系统变量关系的灰箱模型。以实际橡胶硫化促进剂制备的间歇反应过程作为实验对象,建立以生成物浓度为输出的数学模型,达到较高的输出预测精度。
An approach of grey-box modeling based on fundamental genes was developed for modeling dynamic processes with non-linear characteristics.By combination the prior knowledge and systematic behaviors,structure approaching neural network(SANN) was established based on fundamental genes,and the nodes of SANN were given actual significance.Then the optimal fundamental genes were chosen through minimizing the proposed predicted error with stepwise regression analysis(SRA) to optimize the structure of SANN,so as to get the grey-box model.Detailed process of modeling was described in modeling of batch condensation reaction of producing promoter for vulcanizing rubber.The simulation result proves that the approach is effective.
出处
《中南大学学报(自然科学版)》
EI
CAS
CSCD
北大核心
2011年第2期414-418,共5页
Journal of Central South University:Science and Technology
基金
国家自然科学基金资助项目(60704011
60974031)
关键词
灰箱建模
非线性系统
系统辨识
间歇反应
结构逼近神经网络
grey-box modeling
nonlinear system
system identification
batch reaction
structure approaching neural network