Improved fruit fly optimization algorithm optimized wavelet neural network for statistical data modeling for industrial polypropylene melt index prediction. (26th June 2015)

• Record Type: Journal Article
• Authors: Wang, Wenchuan; Zhang, Miao; Liu, Xinggao
• Is Part Of: Journal of chemometrics. Volume 29:Number 9(2015:Sep.)
• Page Start: 506
• Page End: 513
• Publication Date: 2015-06-26
• Subjects: propylene polymerization -- WNN -- IFOA -- melt index prediction; Chemistry -- Mathematics -- Periodicals; Chemistry -- Statistical methods -- Periodicals; 542.85
• DOI: 10.1002/cem.2729
• Languages: English
• ISSNs: 0886-9383

Abstract

Abstract : This paper presents the development of wavelet neural network (WNN) with an improved fruit fly optimization algorithm (IFOA) for the melt index prediction in the industrial propylene polymerization process. The structure, calculation, and prediction process of WNN are proposed, and the improved details of IFOA are introduced, which can enhance the searching efficiency and improve the searching quality over the traditional fruit fly optimization algorithm. Finally, the WNN–IFOA model can obtain the least predicting errors compared with other existing models and shows better generality for the online melt index prediction from the experimental results. Copyright © 2015 John Wiley & Sons, Ltd. Abstract : This paper presents the development of wavelet neural network with an improved fruit fly optimization algorithm for the melt index prediction in the industrial propylene polymerization process.