[1]李微,乔俊飞,韩红桂,等.模糊规则相似性计算与性能分析研究[J].智能系统学报,2017,12(1):124-131.[doi:10.11992/tis.201512040]
LI Wei,QIAO Junfei,HAN Honggui,et al.Computing and performance analysis of similarity between fuzzy rules[J].CAAI Transactions on Intelligent Systems,2017,12(1):124-131.[doi:10.11992/tis.201512040]
点击复制
LI Wei,QIAO Junfei,HAN Honggui,et al.Computing and performance analysis of similarity between fuzzy rules[J].CAAI Transactions on Intelligent Systems,2017,12(1):124-131.[doi:10.11992/tis.201512040]
模糊规则相似性计算与性能分析研究
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
12
期数:
2017年第1期
页码:
124-131
栏目:
学术论文—机器学习
出版日期:
2017-02-25
- Title:
- Computing and performance analysis of similarity between fuzzy rules
- Keywords:
- fuzzy rules; similarity computing; distinguishability; dimension dependency; computing complexity
- 分类号:
- TP18
- 摘要:
- 针对模糊规则相似性分析和计算问题，本文对模糊规则相似性计算方法进行了研究。首先，将模糊规则相似性等价地转化为多变量模糊集相似性，并对模糊规则相似性计算方法提出3种应用性能评价指标——可区分性、维数依赖性和计算复杂性。其次，在现有两种模糊规则相似性计算方法的基础上，提出4种新的计算方法，对各种方法进行系统地性能分析和比较。最后，对模糊规则相似性计算进行仿真研究，结果表明了所提应用性能指标的有效性、计算方法的可行性及分析结果的正确性。本文研究结果为模糊规则相似性分析和计算提供了依据，尤其为基于模糊规则相似性辨识和合并的模糊系统与模糊神经网络结构简化奠定了基础，提供了一种新的设计思路。
- Abstract:
- Facing the weaknesses of the existing analysis and computing methods for the similarity between fuzzy rules (FRs), this paper investigated the computing methods for the similarity between FRs. First, the similarity between FSs was transferred equivalently into the similarity between multivariable fuzzy sets, and then three application based performance criterions-distinguishability, dimension dependency, and computing complexity were proposed to evaluate the computing methods of the similarity between FRs. Second, four new methods were proposed based on the two existing methods for computing the similarity between FRs, and then the performance analysis and comparison between these new and existing methods were performed. Next, a simulation example for the similarity computing between FRs was provided, and the simulation shows effectiveness of the proposed performance criteria, feasibility of the computing methods, and correctness of the analysis conclusions. The results obtained in this paper provide powerful tools and guides for the similarity analysis and computing of FRs. Inparticular, they establish the methodological foundation and provide a new design approach for the merging of similar FRs in the structure simplification of fuzzy systems and fuzzy neural networks.
- 参考文献/References:
-
[1] ZADEH L A. Fuzzy sets[J]. Information and control, 1965, 8(3): 338-353.
[2] TüRKSEN I B, FAZEL ZARANDI M H. Fuzzy system models for aggregate scheduling analysis[J]. International Journal of approximate reasoning, 1998, 19(1/2): 119-143.
[3] FAY A. A fuzzy knowledge-based system for railway traffic control[J]. Engineering applications of artificial intelligence, 2000, 13(6): 719-729.
[4] GE Aaidong, WANG Yuzhen, WEI Airong, et al. Control design for multi-variable fuzzy systems with application to parallel hybrid electric vehicles[J]. Control theory & applications, 2013, 30(8): 998-1004.
[5] CHAO C T, CHEN Y J, TENG C C. Simplification of fuzzy-neural systems using similarity analysis[J]. IEEE transactions on systems, man, and cybernetics, part B (cybernetics), 1996, 26(2): 344-354.
[6] CHEN Minyou, LINKENS D A. Rule-base self-generation and simplification for data-driven fuzzy models[J]. Fuzzy sets and systems, 2004, 142(2): 243-265.
[7] TSEKOURAS G E. Fuzzy rule base simplification using multidimensional scaling and constrained optimization[J]. Fuzzy sets and systems, 2016, 297: 46-72.
[8] SETNES M, BABUSKA R, KAYMAK U, et al. Similarity measures in fuzzy rule base simplification[J]. IEEE transactions on systems, man, and cybernetics, part B (cybernetics), 1998, 28(3): 376-386.
[9] REZAEE B. Rule base simplification by using a similarity measure of fuzzy sets[J]. Journal of intelligent & fuzzy systems: applications in engineering and technology, 2012, 23(5): 193-201.
[10] PRATAMA M, ANAVATTI S G, LUGHOFER E. GENEFIS: toward an effective localist network[J]. IEEE transactions on fuzzy systems, 2014, 22(3): 547-562.
[11] LIN C T, LEE C S G. Reinforcement structure/parameter learning for neural-network-based fuzzy logic control systems[J]. IEEE transactions on fuzzy systems, 1994, 2(1): 46-63.
[12] LENG Gang, ZENG Xiaojun, KEANE J A. A hybrid learning algorithm with a similarity-based pruning strategy for self-adaptive neuro-fuzzy systems[J]. Applied soft computing, 2009, 9(4): 1354-1366.
[13] HAN Honggui, QIAO Junfei. A self-organizing fuzzy neural network based on a growing-and-pruning algorithm[J]. IEEE transactions on fuzzy systems, 2010, 18(6): 1129-1143.
[14] WANG Xiaojing, ZOU Zhihong, ZOU Hui. Water quality evaluation of Haihe River with fuzzy similarity measure methods[J]. Journal of environmental sciences, 2013, 25(10): 2041-2046.
[15] NGUYEN N N, ZHOU W J, QUEK C. GSETSK: a generic self-evolving TSK fuzzy neural network with a novel Hebbian-based rule reduction approach[J]. Applied soft computing, 2015, 35: 29-42.
[16] ROUBENS M. Pattern classification problems and fuzzy sets[J]. Fuzzy sets and systems, 1978, 1(4): 239-253.
[17] DENG Yong, SHI Wenkang, DU Feng, et al. A new similarity measure of generalized fuzzy numbers and its application to pattern recognition[J]. Pattern recognition letters, 2004, 25(8): 875-883.
[18] CASTELLANO G, FANELLI A M, MENCAR C, et al. Similarity-based Fuzzy clustering for user profiling[C]//Proceedings of 2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology Workshops. Silicon Valley, USA, 2007: 75-78.
[19] QIAO Junfei, LI Wei, ZENG Xiaojun, et al. Identification of fuzzy neural networks by forward recursive input-output clustering and accurate similarity analysis[J]. Applied soft computing, 2016, 49: 524-543.
[20] LI Wei, QIAO Junfei, ZENG Xiaojun. Accurate similarity analysis and computing of Gaussian membership functions for FNN simplification[C]//Proceedings of the 12th International Conference on Fuzzy Systems and Knowledge Discovery. Zhangjiajie, China, 2015: 402-409.
- 相似文献/References:
-
[1]王士同,谢振平,李涵雄.模糊推理的统计敏感性分析[J].智能系统学报,2007,2(2):57.
WANG Shi-tong,XIE Zhen-ping,LI Han-xiong.Statistical sensitivity analysis of fuzzy reasoning[J].CAAI Transactions on Intelligent Systems,2007,2():57.
[2]孙富春,杨 晋,刘华平.SISO Mamdani模糊系统作为函数逼近器的必要条件[J].智能系统学报,2009,4(4):288.
SUN Fu-chun,YANG Jin,LIU Hua-ping.Preconditions for SISO Mamdani fuzzy systems to perform as function approximators[J].CAAI Transactions on Intelligent Systems,2009,4():288.
[3]赵潇菲,张井岗.柔性倒立摆的模糊控制算法[J].智能系统学报,2010,5(4):347.
ZHAO Xiao-fei,ZHANG Jing-gang.A fuzzy control method for flexiblejoint inverted pendulums[J].CAAI Transactions on Intelligent Systems,2010,5():347.
[4]段海滨,马冠军,赵振宇.基于模糊规则和动态蚁群贝叶斯网络的无人作战飞机态势评估[J].智能系统学报,2013,8(2):119.[doi:10.3969/j.issn.1673-4785.201211031]
DUAN Haibin,MA Guanjun,ZHAO Zhenyu.UCAV situation assessment based on fuzzy rules and dynamic ant colony-Bayesian network[J].CAAI Transactions on Intelligent Systems,2013,8():119.[doi:10.3969/j.issn.1673-4785.201211031]
[5]胡星辰,李妍,陈紫健,等.粒度模糊规则建模方法研究综述[J].智能系统学报,2024,19(1):22.[doi:10.11992/tis.202306034]
HU Xingchen,LI Yan,CHEN Zijian,et al.Review of the research of granular fuzzy rule-based modeling[J].CAAI Transactions on Intelligent Systems,2024,19():22.[doi:10.11992/tis.202306034]
备注/Memo
收稿日期:2015-12-22;改回日期:。
基金项目:国家自然科学基金项目（6162200417，61533002，61225016）；中国博士后科学基金项目（2014M550017）；北京市教育委员会科研计划项目（KZ201410005002，km201410005001）；高等学校博士学科点专项科研基金项目（20131103110016）.
作者简介:李微,女,1985年生,博士研究生,主要研究方向为智能信息处理;乔俊飞,男,1968年生,教授,博士生导师,中国人工智能学会科普工作委员会主任,主要研究方向为智能信息处理、智能控制理论与应用。获教育部科技进步奖一等奖和北京市科学技术奖三等奖各1项。发表学术论文100余篇,被SCI检索20余篇,EI检索60余篇;韩红桂,男,1983年生,教授,博士生导师,主要研究方向为智能特征建模、自组织模糊控制和多目标智能优化。发表学术论文60余篇。
通讯作者:乔俊飞.E-mail:isibox@sina.com.
更新日期/Last Update:
1900-01-01