基于智能优化灰色模型的电子固废预测

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

系统仿真学报 ›› 2022, Vol. 34 ›› Issue (3): 536-542.doi: 10.16182/j.issn1004731x.joss.20-0833

基于智能优化灰色模型的电子固废预测

孙晓安(), 栾小丽(), 刘飞

江南大学轻工过程先进控制教育部重点实验室，江苏无锡 214122

收稿日期:2020-10-30 修回日期:2020-12-01 出版日期:2022-03-18 发布日期:2022-03-22
通讯作者: 栾小丽 E-mail:2585499836@qq.com;xlluan@jiangnan.edu.cn
作者简介:孙晓安(1997-)，女，硕士生，研究方向为电子固废预测与回收流程优化。E-mail：2585499836@qq.com
基金资助:
国家重点研发计划(2018YFC1900800)

Electronic Solid Waste Prediction Based on Intelligent Optimization Grey Model

Xiaoan Sun(), Xiaoli Luan(), Fei Liu

Key Laboratory for Advanced Process Control of Light Industry of Ministry of Education, Jiangnan University, Wuxi 214122, China

Received:2020-10-30 Revised:2020-12-01 Online:2022-03-18 Published:2022-03-22
Contact: Xiaoli Luan E-mail:2585499836@qq.com;xlluan@jiangnan.edu.cn

摘要/Abstract

摘要：

针对已有电子固废产生量预测时存在的建模机理复杂、建模精度低等问题，提出一种分数阶多元灰色模型与神经网络补偿模型相混合的智能建模

方法

。利用粒子群算法对灰色模型的累加阶数以及背景值参数寻优，发挥灰色模型的最大性能；利用BP神经网络对灰色建模的误差进行补偿，提高固废产生量的预测精度；利用华盛顿州电子固废数据验证了所提方法的有效性。通过对电子固废产生量的精确估计，为电子固废回收的基础设施规划、回收流程优化等提供参考。

关键词: 电子固废预测, 混合智能建模, 分数阶多元灰色模型, BP神经网络, 粒子群算法

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.

Key words: electronic solid waste prediction, hybrid intelligent modeling, fractional multiple gray model, BP neural networks, particle swarm optimization

中图分类号:

TP391.9

孙晓安, 栾小丽, 刘飞. 基于智能优化灰色模型的电子固废预测[J]. 系统仿真学报, 2022, 34(3): 536-542.

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.

图/表 7

图1

表1

粒子群算法参数设置

群体个体数目	最大迭代次数	学习因子1	学习因子2	动态惯性权重因子	粒子范围
30	300	2	2	0.75	$0 < ω i < 1 0 ≤ r ≤ 1$

表1

图2

表2

华盛顿州电子固废产生量数据集

年份	人口密度/ ( $人 ? m i - 2$ )	家庭收入中位数(美元)	固废产生量/t
2003	92.07	46 967.25	18 108.19
2004	93.30	49 585.35	27 341.56
2005	94.66	50 004.20	35 887.90
2006	96.48	53 522.31	46 126.41
2007	98.06	56 141.32	53 737.51
2008	99.31	57 857.52	62 071.46
2009	100.27	55 458.05	69 246.27
2010	101.19	54 888.45	68 777.91
2011	101.84	55 500.26	69 673.02
2012	102.59	56 443.76	73 851.24
2013	103.56	57 283.94	65 894.78
2014	104.85	60 153.00	67 822.93
2015	106.26	63 439.20	72 103.41
2016	108.10	65 500.29	N/A
2017	110.00	68 550.43	N/A

表2

表3

传统GMC(1,3)和优化后FAGMC(1,3)的结果

方法	MAPE/%	$w 1$	$w 2$	$w 3$	$r$
$G M C (1,3)$	2.989	0.5	0.5	0.5	N/A
优化后的 $F A G M C (1,3)$	2.792	0.394	0.580	0.534	1

表3

图3

表4

三种模型预测结果的相对误差比较

模型	预测值/t	相对误差/%
传统 $G M C (1, n)$	77 758.017	7.842
$G M C (1, n) - P S O$	76 952.166	6.301
混合模型	73 584.563	1.777

表4

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基于智能优化灰色模型的电子固废预测

Electronic Solid Waste Prediction Based on Intelligent Optimization Grey Model

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