化工学报2009，Vol.60Issue(11)：2833-2837,5.
利用离散正交多项式组合神经网络建立聚合物分子量分布灰箱模型
Grey box model of polymer molecular weight distribution using hybrid discrete orthogonal polynomial neural network
摘要
Abstract
The molecular weight distribution ( MWD) of polymer is one of the most important performance indexes, which is a kind of typical binary modeling. A method based on hybrid discrete orthogonal polynomial neural network (DOPNN) was proposed to model the MWD of polymers. First, the space and time variables were decomposed by the hybrid neural network. The DOPNN was used to obtain the space model of MWD, and the relationship between MWD and input variables (namely the time variables) was converted into the function between weight vector of space model and input variables. Second, the recurrent neural network was used to obtain the time model. Last, the modeling destination was reached by combining the two NN models mentioned above. The mathematical expression of the model was similar with the traditional discrete state-space expression. Based on the model, an easy way to design the control strategy could be achieved. In space modeling, the weight vector of NN was equivalent to the moment of MWD. That is to say, the weight vector of neural network was of practical significance, so that the grey box model could be obtained. A solution to forecast the number of hidden nodes of neural network was provided. The experimental system investigated was the styrene polymerization in CSTR. The results of the experiment indicated that the NN model was able to capture the MWD as well as to provide accurate moment of MWD through the weight vector of NN model.关键词
聚合物/分子量分布/矩/组合神经网络/离散多项式Key words
polymer/ molecular weight distribution/ moment/ hybrid neural networks/ discrete polynomial分类
化学化工引用本文复制引用
吴海燕,曹柳林,王晶,孙娅苹..利用离散正交多项式组合神经网络建立聚合物分子量分布灰箱模型[J].化工学报,2009,60(11):2833-2837,5.基金项目
国家自然科学基金项目(60704011,60974031). （60704011,60974031）
