基于先验知识的前馈网络对原油实沸点蒸馏曲线的仿真  

Feedforward Networks Based on Prior Knowledge and its Application in Modeling the True Boiling Point Curve of the Crude Oil

作　　者：陈翀伟[1] 陈德钊[1] 叶向群[1] 胡上序[1] 

机构地区：[1]浙江大学化工系,浙江杭州310027

出　　处：《高校化学工程学报》2001年第4期351-356,共6页Journal of Chemical Engineering of Chinese Universities

基　　金：国家自然科学基金资助项目(编号:20076041).

摘　　要：将先验知识与神经元网络相结合，可以提高模型的拟合精度和预测能力。本文将针对三层前馈网与单调性先验知识相结合的问题，分析Joerding的惩罚函数法，提出两种新方法：插值点法和有约束优化方法，并成功地应用于原油实沸点蒸馏曲线的仿真，使网络模型在整体和局部上都更贴近于实际对象。FFN has been widely applied in modeling chemical processes because of its universal approximability. The inclusion of prior knowledge is a means of improving the fit precision and the prediction ability of the modal when trained on sparse and noisy data. As to the three-layer feedforward networks and the prior knowledge of monotonicity constraint, the Joerding's penalty function method is analyzed first. Then two novel methods: interpolation method and constrained optimization method, are proposed. These methods have been applied to modeling the true boiling point curve of the crude oil successfully. The simulation experimental results show that the network models trained by those methods are more close to the actual object in local and whole.

关 键 词：仿真 先验知识 前馈网络 插值点法 有约束优化方法 原油 实沸点 蒸馏曲线 神经网络 

分 类 号：TE622.5[石油与天然气工程—油气加工工程] TP183[自动化与计算机技术—控制理论与控制工程]