化工学报2018，Vol.69Issue(3)：907-912,封2,7.DOI:10.11949/j.issn.0438-1157.20171416
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
Research and application of feature extraction derived functional link neural network
摘要
Abstract
Traditional functional link neural network (FLNN) cannot effectively model multi-dimensional, noisy and strongly coupled data in chemical process. A principal component analysis based FLNN (PCA-FLNN) model was proposed to improve modeling effectiveness. Feature extraction of FLNN function extension block not only removed linear correlations between variables but also selected main components of data, which complexity of FLNN learning data was alleviated. The proposed PCA-FLNN model was used to simulate an UCI Airfoil Self-Noise data and purified terephthalic acid (PTA) production process. Simulation results indicated that PCA-FLNN can achieve faster convergence speed with higher modeling accuracy than traditional FLNN.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Key words
functional link artificial neural network/feature extraction/process modeling/purified terephthalic acid分类
信息技术与安全科学引用本文复制引用
朱群雄,张晓晗,顾祥柏,徐圆,贺彦林..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[J].化工学报,2018,69(3):907-912,封2,7.基金项目
国家自然科学基金重点基金项目(61533003) （61533003）
国家自然科学基金青年基金项目(61703027) （61703027）
中央高校基本科研业务费专项资金(ZY1704,JD1708).supported by the National Natural Science Foundation of China(61533003,61703027)and the Fundamental Research Funds for the Central Universities(ZY1704,JD1708). （ZY1704,JD1708）
