[1]陈楠楠,巩晓婷,傅仰耿.基于改进规则激活率的扩展置信规则库推理方法[J].智能系统学报,2019,14(06):1179-1188.[doi:10.11992/tis.201906046]
 CHEN Nannan,GONG Xiaoting,FU Yanggeng.Extended belief rule-based reasoning method based on an improved rule activation rate[J].CAAI Transactions on Intelligent Systems,2019,14(06):1179-1188.[doi:10.11992/tis.201906046]
点击复制

基于改进规则激活率的扩展置信规则库推理方法(/HTML)
分享到:

《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第14卷
期数:
2019年06期
页码:
1179-1188
栏目:
出版日期:
2019-11-05

文章信息/Info

Title:
Extended belief rule-based reasoning method based on an improved rule activation rate
作者:
陈楠楠1 巩晓婷2 傅仰耿12
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 决策科学研究所, 福建 福州 350116
Author(s):
CHEN Nannan1 GONG Xiaoting2 FU Yanggeng12
1. College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350116, China;
2. Decision Sciences Institute, Fuzhou University, Fuzhou 350116, China
关键词:
置信规则库数据驱动证据推理个体匹配度k近邻思想零激活一致性完整性
Keywords:
belief rule basedata drivenevidence reasoningindividual matching degreek-nearest neighborsnone activationconsistencycompleteness
分类号:
TP18
DOI:
10.11992/tis.201906046
摘要:
数据驱动的扩展置信规则库系统,是在传统置信规则库的基础上利用关系数据来生成规则,使用该方法构建规则库简单有效。然而,该方法激活的规则存在不一致与不完整,并且该方法无法处理零激活的输入。鉴于此,本文提出基于改进规则激活率的扩展置信规则库方法,通过高斯核改进个体匹配度计算方法,权衡激活规则的一致性与完整性,并利用k近邻思想解决规则零激活问题。最后,本文选取非线性函数拟合实验和输油管道检漏实验来检验所提方法的效率和准确度。实验结果表明该方法既保证了扩展置信规则库系统的推理效率,也提高了推理结果的精度。
Abstract:
The data-driven extended belief rule-based system uses relational data to generate rules based on the traditional belief rule base. Using this method to build a rule base is simple and effective. However, the rules activated by this method are inconsistent and incomplete, and this method cannot handle none-activated inputs. Therefore, this paper proposes an extended belief rule-based method, based on an improved rule activation rate. This method improves upon the individual matching degree calculation method through gauss kernels, balances the consistency and completeness of activation rules, and solves the problem of non-activation of rules based on the idea of k-nearest neighbors. Finally, this paper selects a nonlinear function fitting experiment and an oil pipeline leak detection experiment to test the efficiency and accuracy of the proposed method. Experimental results showed that the proposed method not only ensures efficiency, but also improves the accuracy of the extended belief rule-based system.

参考文献/References:

[1] YANG Jianbo, LIU Jun, WANG Jin, et al. Belief rule-base inference methodology using the evidential reasoning approach-RIMER[J]. IEEE transactions on systems, man, and cybernetics-part A:systems and humans, 2006, 36(2):266-285.
[2] HWANG C L, YOON K. Methods for multiple attribute decision making[M]//HWANG C L, YOON K. Multiple Attribute Decision Making. Berlin, Heidelberg:Springer, 1981:58-191.
[3] ZADEH L A, KLIR G J, YUAN Bo. Fuzzy sets, fuzzy logic, and fuzzy systems:selected papers[M]. River Edge, NJ:World Scientific, 1996.
[4] DEMPSTER A P. A generalization of Bayesian inference[J]. Journal of the royal statistical society:series B (methodological), 1968, 30(2):205-247.
[5] ROTA G C. A mathematical theory of evidence:G. Shafer, Princeton University Press, 1976, 297 pp[J]. Shafer, Princeton University Press, 1977, 24(3):341.
[6] 周志杰, 杨剑波, 胡昌华, 等. 置信规则库专家系统与复杂系统建模[M]. 北京:科学出版社, 2011. ZHOU Zhijie, YANG Jianbo, HU Changhua, et al. Belief rule base expert system and complex system modeling[M]. Beijing:Science Press, 2011.
[7] XU Dongling, LIU Jun, YANG Jianbo, 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.
[8] 杨隆浩, 蔡芷铃, 黄志鑫, 等. 出租车乘车概率预测的置信规则库推理方法[J]. 计算机科学与探索, 2015, 9(8):985-994 YANG Longhao, CAI Zhiling, HUANG Zhixin, et al. Belief rule-base inference methodology for predicting probability of taking taxi[J]. Journal of frontiers of computer science and technology, 2015, 9(8):985-994
[9] WANG Yingming, YANG Jianbo, XU Dongling, et al. Consumer preference prediction by using a hybrid evidential reasoning and belief rule-based methodology[J]. Expert systems with applications, 2009, 36(4):8421-8430.
[10] YANG Jianbo, WANG Yingming, XU Dongling, et al. Belief rule-based methodology for mapping consumer preferences and setting product targets[J]. Expert systems with applications, 2012, 39(5):4749-4759.
[11] YANG Jianbo, LIU Jun, XU Dongling, et al. Optimization models for training belief-rule-based systems[J]. IEEE transactions on systems, man, and cyberneticspart A:systems and humans, 2007, 37(4):569-585.
[12] CHEN Yuwang, YANG Jianbo, XU Dongling, et al. Inference analysis and adaptive training for belief rule based systems[J]. Expert systems with applications, 2011, 38(10):12845-12860.
[13] 常瑞, 张速. 基于优化步长和梯度法的置信规则库参数学习方法[J]. 华北水利水电大学学报, 2011, 32(1):154-157 CHANG Rui, ZHANG Su. An algorithm for training parameters in belief rule-bases based on gradient methods with optimization step size[J]. Journal of North China Institute of Water Conservancy and Hydroelectric Power, 2011, 32(1):154-157
[14] 王韩杰, 杨隆浩, 傅仰耿, 等. 专家干预下置信规则库参数训练的差分进化算法[J]. 计算机科学, 2015, 42(5):88-93 WANG Hanjie, YANG Longhao, FU Yanggeng, et al. Differential evolutionary algorithm for parameter training of belief rule base under expert intervention[J]. Computer science, 2015, 42(5):88-93
[15] 苏群, 杨隆浩, 傅仰耿, 等. 基于变速粒子群优化的置信规则库参数训练方法[J]. 计算机应用, 2014, 34(8):2161-2165 SU Qun, YANG Longhao, FU Yanggeng, et al. Parameter training approach based on variable particle swarm optimization for belief rule base[J]. Journal of computer applications, 2014, 34(8):2161-2165
[16] CHANG Leilei, ZHOU Yu, JIANG Jiang, et al. Structure learning for belief rule base expert system:a comparative study[J]. Knowledge-based systems, 2013, 39:159-172.
[17] 杨隆浩, 王晓东, 傅仰耿. 基于关联系数标准差融合的置信规则库规则约简方法[J]. 信息与控制, 2015, 44(1):21-28, 37 YANG Longhao, WANG Xiaodong, FU Yanggeng. Rule reduction approach to belief rule base using correlation coefficient and standard deviation integrated method[J]. Information and control, 2015, 44(1):21-28, 37
[18] 王应明, 杨隆浩, 常雷雷, 等. 置信规则库规则约简的粗糙集方法[J]. 控制与决策, 2014, 29(11):1943-1950 WANG Yingming, YANG Longhao, CHANG Leilei, et al. Rough set method for rule reduction in belief rule base[J]. Control and decision, 2014, 29(11):1943-1950
[19] LIU Jun, MARTINEZ L, CALZADA A, et al. A novel belief rule base representation, generation and its inference methodology[J]. Knowledge-based systems, 2013, 53:129-141.
[20] YANG Longhao, WANG Yingming, FU Yanggeng. A consistency analysis-based rule activation method for extended belief-rule-based systems[J]. Information sciences, 2018, 445-446:50-65.
[21] 林燕清, 傅仰耿. 基于NSGA-Ⅱ的扩展置信规则库激活规则多目标优化方法[J]. 智能系统学报, 2018, 13(3):422-430 LIN Yanqing, FU Yanggeng. NSGA-II-based EBRB rules activation multi-objective optimization[J]. CAAI transactions on intelligent systems, 2018, 13(3):422-430
[22] 苏群, 杨隆浩, 傅仰耿, 等. 基于BK树的扩展置信规则库结构优化框架[J]. 计算机科学与探索, 2016, 10(2):257-267 SU Qun, YANG Longhao, FU Yanggeng, et al. Structure optimization framework of extended belief rule base based on BK-tree[J]. Journal of frontiers of computer science and technology, 2016, 10(2):257-267
[23] YANG Longhao, WANG Yingming, SU Qun, et al. Multi-attribute search framework for optimizing extended belief rule-based systems[J]. Information sciences, 2016, 370/371:159-183.
[24] LIN Yanqing, FU Yanggeng, SU Qun, et al. A rule activation method for extended belief rule base with VP-tree and MVP-tree[J]. Journal of intelligent and fuzzy systems, 2017, 33(6):3695-3705.
[25] CALZADA A, LIU Jun, WANG Hui, et al. A new dynamic rule activation method for extended belief rule-based systems[J]. IEEE transactions on knowledge and data engineering, 2015, 27(4):880-894.
[26] 林燕清, 傅仰耿. 基于改进相似性度量的扩展置信规则库规则激活方法[J]. 中国科学技术大学学报, 2018, 48(1):20-27 LIN Yanqing, FU Yanggeng. A rule activation method for extended belief rule base based on improved similarity measures[J]. Journal of University of Science and Technology of China, 2018, 48(1):20-27
[27] JIAO Lianmeng, PAN Quan, DENOEUX T, et al. Belief rule-based classification system:extension of FRBCS in belief functions framework[J]. Information sciences, 2015, 309:26-49.
[28] COVER T M, HART P E. Nearest neighbor pattern classification[J]. IEEE transactions on information theory, 1967, 13(1):21-27.

相似文献/References:

[1]廖倩芳,李 柠,李少远.一种数据驱动的Ⅱ型T-S模糊建模方法[J].智能系统学报,2009,4(04):303.
 LIAO Qian-fang,LI Ning,LI Shao-yuan.A TypeⅡ TS fuzzy modeling method for datadriven approaches[J].CAAI Transactions on Intelligent Systems,2009,4(06):303.
[2]林锦,胡家琛,刘莞玲,等.利用MISA多目标优化的置信规则库分类算法[J].智能系统学报,2019,14(05):982.[doi:10.11992/tis.201809022]
 LIN Jin,HU Jiachen,LIU Wanling,et al.Belief rule base classification algorithm using MISA multi-objective optimization[J].CAAI Transactions on Intelligent Systems,2019,14(06):982.[doi:10.11992/tis.201809022]
[3]郭宪,方勇纯.仿生机器人运动步态控制:强化学习方法综述[J].智能系统学报,2020,15(1):152.[doi:10.11992/tis.201907052]
 GUO Xian,FANG Yongchun.Locomotion gait control for bionic robots: a review of reinforcement learning methods[J].CAAI Transactions on Intelligent Systems,2020,15(06):152.[doi:10.11992/tis.201907052]

备注/Memo

备注/Memo:
收稿日期:2019-06-24。
基金项目:国家自然科学基金项目(61773123);福建省自然科学基金项目(2019J01647).
作者简介:陈楠楠,男,1993年生,硕士研究生,主要研究方向为智能决策与专家系统;巩晓婷,女,1982年生,讲师,主要研究方向为不确定多准则决策、信息隐藏技术。参与国家自然科学基金项目3项、福建省自然科学基金项目2项和教育部高等学校博士学科点专项科研基金项目1项。发表学术论文10余篇;傅仰耿,男,1981年生,副教授,博士,CCF会员,CAAI会员,主要研究方向为决策理论与方法、数据挖掘、机器学习、智能系统。主持国家自然科学基金项目1项、福建省自然科学基金项目2项。获国家发明专利授权2项,发表学术论文3 0余篇。
通讯作者:傅仰耿.E-mail:ygfu@qq.com
更新日期/Last Update: 2019-12-25