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山东大学学报(工学版) ›› 2015, Vol. 45 ›› Issue (1): 30-36.doi: 10.6040/j.issn.1672-3961.1.2014.212

• 机器学习与数据挖掘 • 上一篇    下一篇

置信规则库参数学习的并行差分进化算法

杨隆浩1, 傅仰耿1, 巩晓婷2   

  1. 1. 福州大学数学与计算机科学学院, 福建 福州 350116;
    2. 福州大学经济与管理学院, 福建 福州 350116
  • 收稿日期:2014-03-26 修回日期:2014-10-15 发布日期:2014-03-26
  • 作者简介:杨隆浩(1990-),男,福建南平人,硕士研究生,主要研究方向为智能决策技术,置信规则库推理等.E-mail:more026@gmail.com
  • 基金资助:
    国家自然科学基金青年资助项目(61300026,61300104);国家杰出青年科学基金资助项目(70925004); 国家自然科学基金面上资助项目(71371053);福建省教育厅科技资助项目(JA13036)

Parallel differential evolution algorithm for parameter learning of belief rule base

YANG Longhao1, FU Yanggeng1, GONG Xiaoting2   

  1. 1. College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350116, Fujian, China;
    2. College of Economics and Management, Fuzhou University, Fuzhou 350116, Fujian, China
  • Received:2014-03-26 Revised:2014-10-15 Published:2014-03-26
  • Contact: 巩晓婷(1982-),女,河南漯河人,讲师,硕士,主要研究方向为不确定多准则决策,信息隐藏技术等.E-mail:xtgong@126.com E-mail:xtgong@126.com

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摘要： 为解决置信规则库中现有参数学习方法主要是串行算法且不适用于求解大数据下参数优化模型的问题,结合群智能算法中的差分进化算法和集群系统中分布式方法,提出了基于消息传递接口的并行参数学习方法。以输油管道检漏问题为例,对比分析了本算法与现有参数学习方法在收敛时的误差,并在不同结点数的集群系统中分析了本算法的加速比和效率。实验结果表明,并行的参数学习方法是有效可行的。

关键词: 输油管道检漏, 置信规则库, 消息传递接口, 差分进化算法, 并行算法, 参数学习, 集群系统

Abstract: To solve the problem of the existing parameter learning approaches for Belief Rule Base (BRB) were mainly serial algorithms, and those approaches were unsuitable for handling parameter optimization model under the big data. The differential evolution algorithm of swarm intelligence algorithms and the distributed method of cluster systems were introduced to the BRB, and then a parallel parameter learning approach using message passing interface was proposed. A numeric example of the pipeline leak detection problem was given. The new approach was compared with the existing parameter approaches in terms of the convergence error, the speedup ratio and the efficiency of parallel algorithm with different nodes of the cluster system. The experimental results showed that the approach was feasibilitiness and effectiveness.

Key words: message passing interface, pipeline leak detection, belief rule base, differential evolution algorithm, parallel algorithm, parameter learning, cluster system

中图分类号: 

  • TP18
[1] DEMPSTER A P. A generalization of bayesian inference[J]. Journal of the Royal Statistical Society-Series B(Methodological), 1968, 30(2): 205-247.
[2] SHAFER G. A mathematical theory of evidence[M]. Princeton: Princeton University Press, 1976.
[3] HUANG C L, YONG K S. Methods for multiple attribute decision making[M]. Berlin: Springer Berlin Heidelberg, 1981: 58-191.
[4] ZADEH L Z. Fuzzy sets[J]. Information and Control, 1965, 8(3): 338-353.
[5] SUN R. Robust reasoning: integrating rule-based and similarity-based reasoning[J]. Artificial Intelligence, 1995, 75(2): 241-295.
[6] YANG J B, LIU J, WANG J, et al. Belief rule-base inference methodology using the evidential reasoning approach-RIMER[J]. IEEE Transaction on Systems, Man, and Cybernetics-Part A: Systems and Humans, 2006, 37(4): 569-585.
[7] LIU J, YAMG J B, RUAN D, et al. Self-tuning of fuzzy belief rule bases for engineering system safety analysis[J]. Annals of Operations Research, 2008, 163(1): 143-168.
[8] YANG J B, LIU J, XU D L, et al. Optimization models for training belief-rule-based systems[J]. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 2007, 37(4): 569-585.
[9] ZHOU Z J, HU C H, YANG J B, et al. Online updating belief-rule-based system for pipeline leak detection under expert intervention[J]. Expert Systems with Applications, 2009, 36(4): 7700-7709.
[10] CHEN Y W, YANG J B, XU D L, et al. Inference analysis and adaptive training for belief rule based system[J]. Expert System with Applications, 2011, 38(10):12845-12860.
[11] 常瑞, 王红卫, 杨剑波. 基于梯度法和二分法的置信规则库参数训练方法[J]. 系统工程, 2007, 25(增刊): 287-291. CHANG Rui, WANG Hongwei, YANG Jianbo. An algorithm for training parameters in belief rule-bases based on the gradient and dichotomy methods[J]. System Engineering, 2007, 25(S):287-291.
[12] ZHOU Z J, HU C H, YANG J B, et al. Online updating belief-rule-base using the RIMER approach[J]. IEEE transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 2011, 41(6):1225-1243.
[13] CHANG L L, LI M J, JIANG J, et al. Parameter learning for the belief rule base system in the residual life probability prediction of metalized film capacitor[J]. European Journal of Operational Research, 2015, 73(1):69-80.
[14] PRICE K, STOM R M. LAMPINEN J A. Differential evolution: a practical approach to global optimization[M]. Berlin:Springer, 2005.
[15] XU D L, LIU J, YANG J B, et al. Inference and learning methodology of belief-rule-based expert system for pipeline leak detection[J]. Expert Systems with Applications. 2007, 32(1): 103-113.
[16] WANG Y M, YANG J B, XU D L, et al. The evidential reasoning approach for multiple attribute decision analysis using interval belief degrees[J]. European Journal of Operational Research, 2006(175):35-66.
[17] 马昌凤. 最优化方法及其Matlab程序设计[M]. 北京:科学出版社, 2010.
[18] 李建中, 刘显敏. 大数据的一个重要方面:数据可用性[J]. 计算机研究与发展,2013, 50(6): 1147-1162. LI Jianzhong, LIU Xianmin. An important aspect of big data: data usability[J]. Journal of Computer Research and Development, 2013, 50(6):1147-1162.
[19] 张蕾. 基于并行PSO的模式分类算法及其应用研究[D]. 济南:济南大学, 2005. ZHANG Lei. Based on the pattern classification of parallel PSO algorithm and its application research[D]. Jinan:Jinan University, 2005.
[20] YANG Z Y, LI X L, BOWERS C P, et al. An efficient evolutionary approach to parameter identification on a building thermal model[J]. IEEE Transactions System Man Cybernetics Part C-Applications and Reviews, 2007, 42(6):957-969.
[21] HE R J, YANG Z Y. Differential evolution with adaptive mutation and parameter control using levy probability distribution[J]. Journal of Computer Science and Technology, 2012, 27(5):1035-1055.
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