Hybrid robust model based on an improved functional link neural network integrating with partial least square (IFLNN-PLS) and its application to predicting key process variables.

He, Yan-Lin; Xu, Yuan; Geng, Zhi-Qiang; Zhu, Qun-Xiong

He, Yan-Lin; Xu, Yuan; Geng, Zhi-Qiang; Zhu, Qun-Xiong.

Afiliación

He YL; College of Information Science & Technology, Beijing University of Chemical Technology, Beijing 100029, China; Engineering Research Center of Intelligent PSE, Ministry of Education of China, Beijing 100029, China.
Xu Y; College of Information Science & Technology, Beijing University of Chemical Technology, Beijing 100029, China; Engineering Research Center of Intelligent PSE, Ministry of Education of China, Beijing 100029, China.
Geng ZQ; College of Information Science & Technology, Beijing University of Chemical Technology, Beijing 100029, China; Engineering Research Center of Intelligent PSE, Ministry of Education of China, Beijing 100029, China.
Zhu QX; College of Information Science & Technology, Beijing University of Chemical Technology, Beijing 100029, China; Engineering Research Center of Intelligent PSE, Ministry of Education of China, Beijing 100029, China. Electronic address: zhuqx@mail.buct.edu.cn.

ISA Trans ; 61: 155-166, 2016 Mar.

Article en En | MEDLINE | ID: mdl-26685746

Palabras clave

Functional link neural network; High Density Polyethylene process; Intelligent measurement; Partial least square; Pearson correlation coefficient; Purified Terephthalic Acid process

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: ISA Trans Año: 2016 Tipo del documento: Article País de afiliación: China Pais de publicación: Estados Unidos

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