Electronic Solid Waste Prediction Based on Intelligent Optimization Grey Model

Authors

Xiaoan Sun, Key Laboratory for Advanced Process Control of Light Industry of Ministry of Education, Jiangnan University, Wuxi 214122, China;Follow
Xiaoli Luan, Key Laboratory for Advanced Process Control of Light Industry of Ministry of Education, Jiangnan University, Wuxi 214122, China;Follow
Fei Liu, Key Laboratory for Advanced Process Control of Light Industry of Ministry of Education, Jiangnan University, Wuxi 214122, China;

Abstract

Abstract: Aiming at the problems of complex modeling mechanism and low modeling accuracy in the prediction of electronic solid waste production, an intelligent modeling method combining fractional order multiple gray model and neural network compensation model is proposed. Particle swarm optimization is used to optimize the accumulative order and background parameters of the gray model to maximize the performance of the gray model. BP neural network is used to compensate the error of gray modeling and improve the prediction accuracy of solid waste production. The effectiveness of the proposed method is verified by Washington state electronic solid waste data. The accurate estimation of electronic solid waste production provides reference for infrastructure planning and process optimization of electronic solid waste recovery.

Recommended Citation

Sun, Xiaoan; Luan, Xiaoli; and Liu, Fei (2022) "Electronic Solid Waste Prediction Based on Intelligent Optimization Grey Model," Journal of System Simulation: Vol. 34: Iss. 3, Article 11.
DOI: 10.16182/j.issn1004731x.joss.20-0833
Available at: https://dc-china-simulation.researchcommons.org/journal/vol34/iss3/11

First Page

536

Revised Date

2020-12-01

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.20-0833

Last Page

542

CLC

TP391.9

Recommended Citation

Xiaoan Sun, Xiaoli Luan, Fei Liu. Electronic Solid Waste Prediction Based on Intelligent Optimization Grey Model[J]. Journal of System Simulation, 2022, 34(3): 536-542.

Corresponding Author

Xiaoli Luan,xlluan@jiangnan.edu.cn

DOI

10.16182/j.issn1004731x.joss.20-0833