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.
出处
《华东理工大学学报（自然科学版）》
CAS
CSCD
北大核心
1997年第1期89-94,共6页
Journal of East China University of Science and Technology
基金
国家"八.五"科技攻关项目
关键词
神经网络
聚酯
自调节变悄度
拟牛顿法
工况
neural networks
artificial intelligence
Quasi newton
polyethylene terephthalate
self scaling variable metric
