[1]罗勇,郑金,宁美凤..短期电力负荷组合预测方法研究[J].郑州大学学报(工学版),2013,34(1):78-81.[doi:10.3969/j.issn.1671-6833.2013.01.019]
　YUAN Feng,DING Ze-xin,Ll Yue-mei,et al.Design and Research for the Layout of Spin Riveting MachineBased on Substance-field Analysis[J].Journal of Zhengzhou University (Engineering Science),2013,34(1):78-81.[doi:10.3969/j.issn.1671-6833.2013.01.019]
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短期电力负荷组合预测方法研究()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
34
期数:
2013年1期
页码:
78-81
栏目:
出版日期:
2013-01-16

文章信息/Info

Title:
Design and Research for the Layout of Spin Riveting MachineBased on Substance-field Analysis
作者:
罗勇,郑金,宁美凤.
郑州大学电气工程学院,河南郑州,450001, 郑州大学电气工程学院,河南郑州,450001, 郑州大学电气工程学院,河南郑州,450001
Author(s):
YUAN Feng1,DING Ze-xin1,Ll Yue-mei1,2,ZHU Jun1
1.School of Mechanical Engineering，Zhengzhou University ,Zhengzhou 450001，China; 2.Tangshan Jidong Petroleum Me-chanical Co.,LTD，Tangshan 063200，China
关键词:
短期电力负荷预测;  组合预测;  小波神经网络;  模糊聚类分析
Keywords:
saw chain;  the layout of Spin Riveting Machine;  substance-field model; TRIZ
分类号:
TM76
DOI:
10.3969/j.issn.1671-6833.2013.01.019
文献标志码:
A
摘要:
为了解决短期电力负荷不同预测方法的预测角度片面性、预测精度差等问题,提出了基于小波神经网络(WNN)的组合预测模型.首先用小波神经网络预测模型和历史平均模型分别进行预测,然后再通过小波神经网络对两单一模型的预测值进行组合.相比BP神经网络组合模型,该组合预测模型的预测精度大大提高.该模型同时引入模糊聚类分析的方法选取组合模型的训练样本,减少了训练样本的冗余性,提高了预测模型的精度.
Abstract:
Based on substance-field analysis theory,firstly,the design conflicts are described,according tothe actual demands of design for the layout of Spin Riveting Machine in the automatic assembly system of sawchain.Secondly,substance-field model of chain shaft riveting is set up.Finally,the design space conflicts aresolved by using the 76 standard solutions and the final ideal solution of the layout of Spin Riveting Machine isfound out，which reduces the whole system ’s research and development time. In a word,the successful trialof automatic assembly system of saw chain will greatly enhance saw chain ’s assembly efficiency and quality ,reducing its production cost and prolonging its using life effectively,so it is important to the technology pro-gress of enterprises.

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更新日期/Last Update: 1900-01-01