期刊文献+

协同设计主体资源动态综合评选模型研究

Research on the Model of Dynamic Comprehensive Evaluation for the Collaborative Design Provider
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摘要 本文针对动态环境下大规模协同设计主体资源评选问题,在将协同设计主体资源动态综合评选问题转化成间接排序问题的基础上,构造了基于LS-SVM的协同设计主体资源综合能力水平比较分类模型。并以此模型为基础,进一步采用对分法构造了协同设计主体资源动态综合评选模型。实验验证表明,该模型不仅实现了在动态环境下大规模资源按照综合能力强弱快速准确的排序,而且还解决了以往数学方法存在的主观性强、过学习、维数灾、局部最优等问题,对于实现协同设计主体资源的有效发现具有重要的现实意义。同时,该方法为同类型评选问题提供了一种有效的解决途径。 Based on the transforming the problem of the dynamic comprehensive evaluation for the collaborative design provider into the problem of the indirectly ranking, this paper presents a model of horizontal comparison classifying for the comprehensive ability of the collaborative design providers using the LS-SVM, and then brings forward a model of the dynamic comprehensive evaluation for the collaborative design providers based on the former model using the bisection method, aimed at the problem of choosing through evaluation of the large - scale collaborative design providers under the dynamic condition. The experiment proves that the model not only implements the ranking of the large-scale collaborative design providers reliably and rapidly according to the comprehensive ability under the dynamic condition, but also solves the shortages of traditional mathematic methods such as subjectivity, over-fitting, curse of dimensionality and local optimization. The model has important realistic significance for the valid finding of the collaborative design provider. Also, this method provides a valid solution to the same type problem of choosing through evaluation in other fields.
出处 《运筹与管理》 CSCD 2008年第2期146-151,162,共7页 Operations Research and Management Science
基金 国家自然科学基金资助项目(50572121)
关键词 协同设计 动态评价 支持向量机 主体资源 collaborative design dynamic evaluation SVM collaborative design provider
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参考文献10

  • 1王强,陈英武,李孟军.基于支持向量机的卷烟质量评估方法[J].系统工程理论方法应用,2006,15(5):475-478. 被引量:8
  • 2VapnikVN.统计学习理论的本质[M].北京:清华大学出版社,2000.. 被引量:171
  • 3卢增祥,李衍达.交互支持向量机学习算法及其应用[J].清华大学学报(自然科学版),1999,39(7):93-97. 被引量:41
  • 4Smola A J, Scholkopf B. A tutorial on support vector regression[ J]. Statistics and Computing, 2004, 14 (3) : 199-222. 被引量:1
  • 5夏国恩,金炜东,张葛祥.基于支持向量分类机和回归机的综合评价方法[J].西南交通大学学报,2006,41(4):522-527. 被引量:5
  • 6Suykens J A K, Vandewalle J, De Moor B. Optimal control by least squares support vector machines[J] . Neural Networks, 2001, (14) : 23-35. 被引量:1
  • 7Smola A J. Learning with kernels[ D]. Berlin: Technische Universit at Berlin, 1998. 被引量:1
  • 8Roobacert D, Van Hulle M M. View-based 3d object recognition with suppot vector machines: an application to 3d object recognition with cluttered background[ A]. Proc. SVM Workshop at IJCAI' 99 [ C]. Stockholm, Sweden, 1999. 被引量:1
  • 9Scholkopf B, et al. Face pose discrinination using support vector machines[ A]. Proceedings of CVPR 2000[ C]. Hilton Head Island, 2000. 430-437. 被引量:1
  • 10Daisuke Tsujinishi, Shigeo Abe. Fuzzy least squares support vector machines for multi-class problems[ J]. Neural Networks, 2003, (16) : 785-792. 被引量:1

二级参考文献25

  • 1高家合,秦西云,谭仲夏,李梅云.烟叶主要化学成分对评吸质量的影响[J].山地农业生物学报,2004,23(6):497-501. 被引量:159
  • 2范昕炜.支持向量机的算法及其应用[D].博士学位论文,杭州:浙江大学,2003. 被引量:1
  • 3VAPNIK V.Statistical learning theory[M].New York John Wiley&Sons,1998.80-310. 被引量:1
  • 4VAPNIK V.An overview of statistical learning theory[J].IEEETrans.on NN.,1999,10 (3):988-999. 被引量:1
  • 5PONTIL M,RIFKIN R,EVENIOU T.From regression to classification in support vector machines[C]// Proceedings ESANN.Brussels:D Facto,1999.225-230. 被引量:1
  • 6SMOLA A,SCHOLKOPF B.On a kernel-based method for pattern,regression,approximation and operator iversion[J].Algorithmica,1998(22):211-231. 被引量:1
  • 7Hu Yuhen,IEEE Signal Processing Magazine,1997年,11卷,39页 被引量:1
  • 8边肇祺,模式识别,1988年 被引量:1
  • 9VapnikV.统计学习理论的本质[M].北京:清华大学出版社,2000.. 被引量:27
  • 10VapnikVN.统计学习理论的本质[M].北京:清华大学出版社,2000.. 被引量:171

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