|本期目录/Table of Contents|

[1]乔维德.基于BP神经网络的机电产品绿色度评价方法[J].温州职业技术学院学报,2017,02:33-37.
 Qiao Weide.An Evaluation Method for Green Degree of Mechanical and Electrical Products Based on BP Neural Network[J].Journal of Wenzhou Vocational and Technical College,2017,02:33-37.
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基于BP神经网络的机电产品绿色度评价方法(PDF)

《温州职业技术学院学报》[ISSN:1006-6977/CN:61-1281/TN]

期数:
2017年02期
页码:
33-37
栏目:
出版日期:
2017-04-20

文章信息/Info

Title:
An Evaluation Method for Green Degree of Mechanical and Electrical Products Based on BP Neural Network
作者:
乔维德
无锡开放大学 科研与质量控制处,江苏 无锡 214011
Author(s):
Qiao Weide
Scientific Research and Quality Control Department, Wuxi Open University, Wuxi, 214011, China
关键词:
机电产品绿色度评价指标AHPPSO-ABC
Keywords:
Mechanical and electrical products Green degree Evaluation indicator AHP PSO-ABC
分类号:
TH122
DOI:
10.13669/j.cnki.33-1276/z.2017.030
文献标识码:
A
摘要:
绿色度评价直接影响着机电产品的设计、制造、管理及发展。从机电产品制造的能源、资源、环境、 经济和技术等属性进行分析,运用层次分析法(AHP)确定机电产品绿色度评价指标体系及其权重,建立机电产品绿 色度BP神经网络评价模型,通过粒子群-人工蜂群(PSO-ABC)算法优化训练BP神经网络结构参数。仿真实验表明, 该方法评价速度快、准确率高,对于指导机电产品绿色制造具有较好的参考价值。
Abstract:
Green degree evaluation directly affects design, manufacture, management and development of the mechanical and electronic products. By analyzing the energy, resources, environment, economy, technology, etc. of the mechanical and electrical product manufacturing, and applying analytic hierarchy process (AHP) to determine the green degree evaluation indicator of the mechanical and electrical products and its weight, the research establishes a green degree evaluation model of BP neural network, and optimizes the BP network structure parameters via the particle swarm by artificial swarm algorithm (PSO-ABC) algorithm. Simulation data and experimental results show that this method of evaluation reveals high speed and high accuracy, and is valuable to the green manufacturing of the mechanical and electrical products.

参考文献/References

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备注/Memo

备注/Memo:
[收稿日期]2017-02-24 [基金项目]无锡市社会事业领军人才资助项目(WX530/2016013) [作者简介]乔维德(1967—),男,江苏宝应人,无锡开放大学科研与质量控制处,教授.
更新日期/Last Update: 2017-04-20