基于特征提取的函数连接神经网络研究及其化工过程建模应用

朱群雄1,2 张晓晗1,2 顾祥柏1,3 徐圆1,2 贺彦林1,2

(1.北京化工大学信息科学与技术学院, 北京 100029)
(2.智能过程系统工程教育部工程研究中心, 北京 100029)
(3.中石化炼化工程(集团)股份有限公司, 北京 100101)

【摘要】对于化工过程中带噪声、强耦合的高维数据建模问题,常规的函数连接神经网络(functional link neural networks,FLNN)无法有效地进行处理。为解决该问题,提出一种基于主元分析(principal components analysis,PCA)的函数连接神经网络(PCA-FLNN)。通过对FLNN的函数扩展层进行特征提取,不仅去除变量间的线性相关关系,而且提取数据的主成分,进而简化FLNN学习数据的复杂度。为验证所提方法的有效性,首先采用UCI数据Airfoil Self-Noise对其性能进行验证;随后将所提的方法应用于精对苯二甲酸(purified terephthalic acid,PTA)生产过程建模;与传统FLNN进行对比,标准数据和工业数据的仿真结果表明,PCA-FLNN在处理复杂化工过程数据时具有收敛速度快和建模精度高的特点。

【关键词】 函数连接神经网络; 特征提取; 过程建模; 精对苯二甲酸;

【DOI】

【基金资助】 国家自然科学基金重点基金项目(61533003);国家自然科学基金青年基金项目(61703027) supported by the National Natural Science Foundation of China(61533003,61703027) 中央高校基本科研业务费专项资金(ZY1704,JD1708) the Fundamental Research Funds for the Central Universities(ZY1704,JD1708)

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This Article

ISSN:0438-1157

CN: 11-1946/TQ

Vol 69, No. 03, Pages 907-912+883

March 2018

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Article Outline

摘要

  • 引言
  • 1 相关方法介绍
  • 2 基于主元分析法的FLNN(PCA-FLNN)
  • 3 实验结果分析
  • 4 结论
  • 参考文献