Improved fruit fly optimization algorithm optimized wavelet neural network for statistical data modeling for industrial polypropylene melt index prediction

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.

[1]  Steven D. Brown,et al.  Selecting wavelet transform scales for multivariate classification , 2007 .

[2]  Ingrid Daubechies,et al.  The wavelet transform, time-frequency localization and signal analysis , 1990, IEEE Trans. Inf. Theory.

[3]  Sen Guo,et al.  A hybrid annual power load forecasting model based on generalized regression neural network with fruit fly optimization algorithm , 2013, Knowl. Based Syst..

[4]  Xinggao Liu,et al.  Modeling mass transport of propylene polymerization on Ziegler–Natta catalyst , 2005 .

[5]  Jie Zhang,et al.  Inferential Estimation of Polymer Melt Index Using Sequentially Trained Bootstrap Aggregated Neural Networks , 2006 .

[6]  Xinggao Liu,et al.  Melt index prediction by weighted least squares support vector machines , 2006 .

[7]  Yeong-Koo Yeo,et al.  Prediction and quality control of the melt index during production of high-density polyethylene , 2008 .

[8]  Efstathios Paparoditis,et al.  Wavelet Methods in Statistics with R , 2010 .

[9]  Wen-Tsao Pan,et al.  A new Fruit Fly Optimization Algorithm: Taking the financial distress model as an example , 2012, Knowl. Based Syst..

[10]  Huaqin Jiang,et al.  Melt index prediction by adaptively aggregated RBF neural networks trained with novel ACO algorithm , 2012 .

[11]  Feng Ding,et al.  Combined state and least squares parameter estimation algorithms for dynamic systems , 2014 .

[12]  Stéphane Mallat,et al.  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  Yeong-Koo Yeo,et al.  Development of polyethylene melt index inferential model , 2010 .

[14]  Yeong-Koo Yeo,et al.  Prediction of the Melt Index in a High-Density Polyethylene Process , 2007 .

[15]  Jun Yuan,et al.  Prediction model for increasing propylene from FCC gasoline secondary reactions based on Levenberg–Marquardt algorithm coupled with support vector machines , 2010 .

[16]  Huizhong Yang,et al.  Modelling and identification for non-uniformly periodically sampled-data systems , 2010 .

[17]  F. Ding.  Hierarchical multi-innovation stochastic gradient algorithm for Hammerstein nonlinear system modeling , 2013 .

[18]  Zhiqiang Ge,et al.  Nonlinear semisupervised principal component regression for soft sensor modeling and its mixture form , 2014 .

[19]  Zhong-Ren Peng,et al.  Fine-scale estimation of carbon monoxide and fine particulate matter concentrations in proximity to a road intersection by using wavelet neural network with genetic algorithm , 2015 .

[20]  Hongde Dai,et al.  Comment and improvement on "A new Fruit Fly Optimization Algorithm: Taking the financial distress model as an example" , 2014, Knowl. Based Syst..

[21]  Yan Xue-feng,et al.  Hybrid artificial neural network based on BP-PLSR and its application in development of soft sensors , 2010 .

[22]  Jilei Zhou,et al.  Chaos optimization algorithms based on chaotic maps with different probability distribution and search speed for global optimization , 2014, Commun. Nonlinear Sci. Numer. Simul..

[23]  刘兴高,et al.  Melt Index Prediction by Neural Soft-Sensor Based on Multi-Scale Analysis and Principal Component Analysis , 2005 .

[24]  T. S. Liu,et al.  A wavelet network control method for disk drives , 2006, IEEE Transactions on Control Systems Technology.

[25]  Ting Wang,et al.  Characterization of chaotic multiscale features on the time series of melt index in industrial propylene polymerization system , 2014, J. Frankl. Inst..

[26]  Chunquan Li,et al.  A Novel Modified Fly Optimization Algorithm for Designing the Self-Tuning Proportional Integral Derivative Controller , 2012 .

[27]  Su-Mei Lin,et al.  Analysis of service satisfaction in web auction logistics service using a combination of Fruit fly optimization algorithm and general regression neural network , 2011, Neural Computing and Applications.

[28]  Feng Ding,et al.  Recursive least squares parameter identification algorithms for systems with colored noise using the filtering technique and the auxilary model , 2015, Digit. Signal Process..

[29]  Brian Roffel,et al.  Non-linear model based control of a propylene polymerization reactor , 2007 .

[30]  Bobby G. Sumpter,et al.  A Toxicity Evaluation and Predictive System Based on Neural Networks and Wavelets , 2007, J. Chem. Inf. Model..

[31]  Jan Hannig,et al.  Support vector machine classification of suspect powders using laser‐induced breakdown spectroscopy (LIBS) spectral data , 2012 .

[32]  Xin Yang,et al.  Tuning of PID controller based on Fruit Fly Optimization Algorithm , 2012, 2012 IEEE International Conference on Mechatronics and Automation.

[33]  V. Dua.  A mixed-integer programming approach for optimal configuration of artificial neural networks , 2010 .

[34]  Youxian Sun,et al.  Melt index prediction by neural networks based on independent component analysis and multi-scale analysis , 2006, Neurocomputing.

[35]  Chonghun Han,et al.  Melt index modeling with support vector machines, partial least squares, and artificial neural networks , 2005 .

[36]  Lingen Chen,et al.  Application of bacterial colony chemotaxis optimization algorithm and RBF neural network in thermal NDT/E for the identification of defect parameters , 2011 .

[37]  Liu Cheng-zhong.  Fruit fly optimization algorithm based on bacterial chemotaxis , 2013 .

[38]  Yaonan Wang,et al.  Hybrid parallel chaos optimization algorithm with harmony search algorithm , 2014, Appl. Soft Comput..

[39]  O. Kisi.  Wavelet regression model for short-term streamflow forecasting. , 2010 .

[40]  Yagyensh C. Pati,et al.  Analysis and synthesis of feedforward neural networks using discrete affine wavelet transformations , 1993, IEEE Trans. Neural Networks.

[41]  Wei-Yuan Lin,et al.  Using Fruit Fly Optimization Algorithm Optimized Grey Model Neural Network to Perform Satisfaction Analysis for E-Business Service , 2013 .

[42]  Huaqin Jiang,et al.  Melt index prediction using optimized least squares support vector machines based on hybrid particle swarm optimization algorithm , 2013, Neurocomputing.

[43]  Feng Ding,et al.  Hierarchical estimation algorithms for multivariable systems using measurement information , 2014, Inf. Sci..

[44]  Olivier Monga,et al.  Thin Nets and Crest Lines: Application to Satellite Data and Medical Images , 1997, Comput. Vis. Image Underst..

[45]  Liu Cheng-zhong,et al.  Adaptive chaos fruit fly optimization algorithm , 2013 .