[1]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.[doi:10.11992/tis.201710012]
LIN Yanqing,FU Yanggeng.NSGA-II-based EBRB rules activation multi-objective optimization[J].CAAI Transactions on Intelligent Systems,2018,13(3):422-430.[doi:10.11992/tis.201710012]
点击复制
LIN Yanqing,FU Yanggeng.NSGA-II-based EBRB rules activation multi-objective optimization[J].CAAI Transactions on Intelligent Systems,2018,13(3):422-430.[doi:10.11992/tis.201710012]
基于NSGA-II的扩展置信规则库激活规则多目标优化方法
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
13
期数:
2018年第3期
页码:
422-430
栏目:
学术论文—智能系统
出版日期:
2018-05-05
- Title:
- NSGA-II-based EBRB rules activation multi-objective optimization
- Keywords:
- extended belief rule base (EBRB); inconsistency; activation rules; multi-objective optimization; NSGA-II algorithm
- 分类号:
- TP18
- 摘要:
- 针对扩展置信规则库（extended belief rule base,EBRB）系统在不一致的激活规则过多时推理准确性不高的问题，引入带精英策略的快速非支配排序遗传算法（NSGA-Ⅱ），提出一种基于NSGA-Ⅱ的激活规则多目标优化方法。该方法首先将激活权重大于零的规则（即激活规则）进行二进制编码，把最终参与合成推理的激活规则集合的不一致性以及激活权重和作为多目标优化问题的目标函数，通过带精英策略的快速非支配排序遗传算法求解不一致性更小的激活规则集合，从而降低不一致激活规则对于EBRB系统推理准确性的影响。为了验证本文方法的有效性和可行性，引入非线性函数和输油管道检漏实例进行测试。实验结果表明，基于NSGA-Ⅱ的扩展置信规则库激活规则多目标优化方法能够有效提高EBRB系统的推理能力。
- Abstract:
- To address the low reasoning accuracy of extended belief rule-base (EBRB) systems with too many inconsistent activated rules, this paper introduces a fast elitist non-dominated sorting genetic algorithm (NSGA-Ⅱ) and proposes a rule activation multi-objective optimization approach based on the NSGA-Ⅱ algorithm. In this approach, binary coding is carried out for the activated rules whose activation weights are greater than zero. The inconsistent set of activated rules following synthetic reasoning and the sum of activation weights are taken as the objective function of the multi-objective optimization problem. Using the fast elitist non-dominated sorting genetic algorithm, the problem of a set of activation rules with a small inconsistency is solved, reducing the effect of the inconsistent activated rules on the reasoning accuracy of EBER systems. To validate the efficiency and feasibility of the proposed method, this paper introduces a nonlinear function and the proposed method was tested against the leak detection of an oil pipeline. The experimental results show that the rule activation multi-objective optimization approach based on NSGA-Ⅱ can effectively improve the reasoning performance of EBRB systems.
- 参考文献/References:
-
[1] 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.
[2] 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.
[3] DEMPSTER A P. A generalization of Bayesian inference[J]. Journal of the royal statistical society. Series B (methodological), 1968, 30(2):205-247.
[4] SHAFER G. A mathematical theory of evidence[M]. Princeton:Princeton University Press, 1976.
[5] HWANG C L, YOON K. Multiple attribute decision making:methods and applications a state of the art survey[M]. New York:Springer, 1981:22-34.
[6] ZADEH L A. Fuzzy sets[J]. Information and control, 1965, 8(3):338-353.
[7] ZHOU Zhijie, HU Changhua, YANG Jianbo, 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.
[8] 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.
[9] YANG Jianbo, LIU Jun, XU Dongling, 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.
[10] YANG Ying, FU Chao, CHEN Yuwang, et al. A belief rule based expert system for predicting consumer preference in new product development[J]. Knowledge-based systems, 2016, 94:105-113.
[11] JIANG Jiang, LI Xuan, ZHOU Zhijie, et al. Weapon system capability assessment under uncertainty based on the evidential reasoning approach[J]. Expert systems with applications, 2011, 38(11):13773-13784.
[12] 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.
[13] 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.
[14] 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]. 华北水利水电学院学报, 2011,32(1):154-157. Chang Rui, Zhang Su. An algorithm for training parameters in belief rule-bases based on the gradient methods with optimization step size[J]. Journal of north China institute of water conservancy and hydroelectric power, 2011, 32(1):154-157.
[15] 吴伟昆, 杨隆浩, 傅仰耿, 等. 基于加速梯度求法的置信规则库参数训练方法[J]. 计算机科学与探索, 2014, 8(8):989-1001. WU Weikun, YANG Longhao, FU Yanggeng, et al. Parameter training approach for belief rule base using the accelerating of gradient algorithm[J]. Journal of frontiers of computer science and technology, 2014, 8(8):989-1001.
[16] 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 application, 2014, 34(8):2161-2165.
[17] 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.
[18] ZHOU Zhijie, HU Changhua, YANG Jianbo, et al. A sequential learning algorithm for online constructing belief-rule-based systems[J]. Expert systems with applications, 2010, 37(2):1790-1799.
[19] 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.
[20] 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.
[21] 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.
[22] 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, 7(4):880-894.
[23] YANG Jianbo. Rule and utility based evidential reasoning approach for multiattribute decision analysis under uncertainties[J]. European journal of operational research, 2001, 131(1):31-61.
[24] WANG Yingming, YANG Jianbo, XU Dongling. Environmental impact assessment using the evidential reasoning approach[J]. European journal of operational research, 2006, 174(3):1885-1913.
[25] 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.
[26] ZITZLER E. Evolutionary algorithm for multi-objective optimization:methods and applications[D]. Zurich:Swiss Federal Institute of Technology, 1999.
[27] DEB K, AGRAWAL S, PRATAP A, et al. A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization:NSGA-Ⅱ[C]//Proceedings of the 6th International Conference Paris Parallel Problem Solving from Nature PPSN VI. Kanpur, India, 2000.
[28] 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.
备注/Memo
收稿日期:2017-10-17。
基金项目:国家自然科学基金项目（71501047，61773123）；福建省自然科学基金项目（2015J01248）.
作者简介:林燕清,女,1992年生,硕士研究生,主要研究方向为智能决策与专家系统;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。
通讯作者:傅仰耿.E-mail:ygfu@qq.com.
更新日期/Last Update:
2018-06-25