[1]王玉芳,严洪森.基于非线性模糊支持向量机的知识化制造模式与动态环境匹配分类方法[J].东南大学学报(自然科学版),2014,44(5):957-962.[doi:10.3969/j.issn.1001-0505.2014.05.015]
 Wang Yufang,Yan Hongsen,et al.Classification method of matching knowledgeable manufacturing mode with dynamic environment based on nonlinear fuzzy weight SVM[J].Journal of Southeast University (Natural Science Edition),2014,44(5):957-962.[doi:10.3969/j.issn.1001-0505.2014.05.015]
点击复制

基于非线性模糊支持向量机的知识化制造模式与动态环境匹配分类方法()
分享到:

《东南大学学报(自然科学版)》[ISSN:1001-0505/CN:32-1178/N]

卷:
44
期数:
2014年第5期
页码:
957-962
栏目:
自动化
出版日期:
2014-09-20

文章信息/Info

Title:
Classification method of matching knowledgeable manufacturing mode with dynamic environment based on nonlinear fuzzy weight SVM
作者:
王玉芳12严洪森13
1东南大学复杂工程系统测量与控制教育部重点实验室, 南京 210096; 2南京信息工程大学自动化系, 南京 210044; 3东南大学自动化学院, 南京 210096
Author(s):
Wang Yufang1 2 Yan Hongsen1 3
1Key Laboratory of Measurement and Control of Complex Systems of Engineering of Ministry of Education, Southeast University, Nanjing 210096, China
2Department of Automation, Nanjing University of Information and Science Technology, Nanjing 210044, China
3School of Automation, Southeast University, Nanjing 210096, China
关键词:
知识化制造模式 环境因素 支持向量机 粒子群优化
Keywords:
knowledgeable manufacturing mode environment factors support vector machine(SVM) particle swarm optimization
分类号:
TP183
DOI:
10.3969/j.issn.1001-0505.2014.05.015
摘要:
为了评价企业当前知识化制造模式与动态环境因素的匹配性,为企业的快速响应提供依据,提出了一种考虑模糊输入和不均衡样本的非线性模糊加权支持向量机(NFW-SVM)模型.考虑到实际生产面临的动态环境因素具有模糊性和不确定性,引入三角模糊数对模糊因素进行描述.针对不同匹配类别数据样本的不均衡性,设置了不同的分类惩罚因子,以降低小样本错分的比例.将变异算子和具有收缩因子的动态惯性权重引入到标准粒子群优化算法中,利用改进的粒子群算法对模型参数进行优化,提高模型的分类精度.给出了基于NFW-SVM模型的知识化制造模式与动态环境匹配的分类方法.最后,通过实例验证了该方法的有效性和可行性.
Abstract:
To correctly judge the matching category between current knowledgeable manufacturing mode and dynamic environment factors, and provide the basis for rapid response, a model of nonlinear fuzzy weight-support vector machine(NFW-SVM)is proposed in which fuzzy inputs and imbalance of the different matching categories of samples are considered. Considering the vagueness and uncertainty of the dynamic production environment in the actual production, the triangular fuzzy number is adopted to describe the vague factor. For the imbalance characters of the data sample in different categories, different category penalty factors are set up in the model to reduce the fault proportions of small samples. The mutation operator and dynamic inertia weight with constriction factors are introduced to the standard particle swarm optimization algorithm. To enhance the classification accuracy, the model parameters are optimized by the improved particle swarm optimization algorithm. The classification method based on NFW-SVM to judge the matching category between dynamic environment factors and current manufacturing mode is presented. Finally, the effectiveness and feasibility of the proposed method are verified by an example.

参考文献/References:

[1] 严洪森, 刘飞. 知识化制造系统——新一代先进制造系统 [J]. 计算机集成制造系统, 2001, 7(8): 7-11.
  Yan Hongsen, Liu Fei. Knowledgeable manufacturing system—a new kind of advanced manufacturing system [J]. Computer Integrated Manufacturing Systems, 2001, 7(8): 7-11.(in Chinese)
[2] Yan H S. A new complicated-knowledge representation approach based on knowledge meshes [J]. IEEE Transactions on Knowledge and Data Engineering, 2006, 18(1): 47-62.
[3] Sabzekar M, Naghibzadeh M. Fuzzy c-means improvement using relaxed constraints support vector machines [J]. Applied Soft Computing, 2013, 13(2): 881-890.
[4] Lin C F, Wang S D. Fuzzy support vector machines [J]. IEEE Transactions on Neural Networks, 2002, 13(2): 464-471.
[5] 李刚,贺昌政. 基于模糊加权支持向量机的移动通讯客户满意度问题研究 [J]. 中国经济与管理科学, 2008, 7(8): 28-30.
  Li Gang, He Changzheng. Research on the customer satisfaction index of mobile communications based on fuzzy weighted support machine [J]. Chinese Economy and Management Science, 2008, 7(8): 28-30.(in Chinese)
[6] Liu B D. Minimax chance constrained programming models for fuzzy decision systems [J]. Information Sciences, 1998, 112(1/2/3/4): 25-38.
[7] 李存林, 张强. 基于可能性测度的模糊对策 [J]. 北京理工大学学报, 2011, 31(3): 324-328.
  Li Cunlin, Zhang Qiang. Fuzzy games based on possibility measures [J]. Transactions of Beijing Institute of Technology, 2011, 31(3): 324-328.(in Chinese)
[8] Wang S, Watada J. A hybrid modified PSO approach to VaR-based facility location problems with variable capacity in fuzzy random uncertainty [J]. Information Sciences, 2012, 192(1): 3-18.
[9] Jiang M, Luo Y P, Yang S Y. Stochastic convergence analysis and parameter selection of the standard particle swarm optimization algorithm[J]. Information Processing Letters, 2007, 102(1): 8-16.
[10] Ji A, Pang J, Qiu H. Support vector machine for classification based on fuzzy training data [J]. Expert Systems with Applications, 2010, 37(4): 3495-3498.

相似文献/References:

[1]丁幼亮,李爱群,耿方方.考虑环境因素影响的悬索桥整体状态预警方法[J].东南大学学报(自然科学版),2010,40(5):1052.[doi:10.3969/j.issn.1001-0505.2010.05.032]
 Ding Youliang,Li Aiqun,Geng Fangfang.Monitoring and warning of health conditions for suspension bridges under varying environmental conditions[J].Journal of Southeast University (Natural Science Edition),2010,40(5):1052.[doi:10.3969/j.issn.1001-0505.2010.05.032]

备注/Memo

备注/Memo:
收稿日期: 2014-02-17.
作者简介: 王玉芳(1979—),女,博士生; 严洪森(联系人),男,博士,教授,博士生导师,hsyan@seu.edu.cn.
基金项目: 国家自然科学基金重点资助项目(60934008).
引用本文: 王玉芳,严洪森.基于非线性模糊支持向量机的知识化制造模式与动态环境匹配分类方法[J].东南大学学报:自然科学版,2014,44(5):957-962. [doi:10.3969/j.issn.1001-0505.2014.05.015]
更新日期/Last Update: 2014-09-20