文献类型：期刊文献

中文题名：神经网络自调节变尺度算法及其用于聚酯生产工况预测

英文题名：Learning Algorithm with Self scaling Variable Metric for Neural Networks and Its Application for Predicting the Conditions of Polyethylene Terephthalate

作者：杨秋贵[1];张杰[1];张素贞[1]

机构：[1]华东理工大学自控系

年份：1997

卷号：23

期号：1

起止页码：89

中文期刊名：华东理工大学学报（自然科学版）

外文期刊名：Journal of East China University of Science and Technology

收录：CSTPCD;;国家哲学社会科学学术期刊数据库;Scopus;北大核心:【北大核心1996】；CSCD:【CSCD2011_2012】；

基金：国家"八.五"科技攻关项目

语种：中文

中文关键词：神经网络;聚酯;自调节变悄度;拟牛顿法;工况

外文关键词：neural networks; artificial intelligence; Quasi newton; polyethylene terephthalate; self scaling variable metric

摘要：探讨了多层前向神经网络的学习算法，并将该算法用于大型聚酯生产工况预测。结合非线性最优化方法，提出了一种基于拟牛顿法的神经元网络自调节变尺度学习算法，仿真结果表明，该算法有效地改进了神经元网络学习收敛速度和收敛性能。
In a complex chemical industry process, predicting the conditions of the process is one of the most promising fields for neural networks application. This paper is concerned with improvements of neural networks learning algorithm and its application for predicting the production conditions of polyethylene terephthalate (PET). On the basis of the analysis of the optimization methods, a new algorithm based on Quasi newton method with self scaling variable metric is proposed. Simulation results show the effectiveness and the good convergence of the new algorithm.