[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(01):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(01):124-131.[doi:10.11992/tis.201512040]
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模糊规则相似性计算与性能分析研究(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第12卷
期数:
2017年01期
页码:
124-131
栏目:
出版日期:
2017-02-25

文章信息/Info

Title:
Computing and performance analysis of similarity between fuzzy rules
作者:
李微12 乔俊飞12 韩红桂12 曾晓军3
1. 北京工业大学 信息学部, 北京 100124;
2. 计算智能与智能系统北京市重点实验室, 北京 100124;
3. 曼彻斯特大学 计算机科学学院, 曼彻斯特 M13 9PL
Author(s):
LI Wei12 QIAO Junfei12 HAN Honggui12 ZENG Xiaojun3
1. Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China;
2. Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing 100124, China;
3. School of Computer Science, the University of Manchester, Manchester M13 9PL, UK
关键词:
模糊规则相似性计算可区分性维度依赖性计算复杂性
Keywords:
fuzzy rulessimilarity computingdistinguishabilitydimension dependencycomputing complexity
分类号:
TP18
DOI:
10.11992/tis.201512040
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:

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备注/Memo

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,教育部长江学者特聘教授,国家杰出青年基金获得者,教育部新世纪优秀人才,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,入选国家自然科学基金优秀青年科学基金、中国科协青年人才托举工程等,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