高级搜索

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

基于模糊子空间聚类的〇阶L2型TSK模糊系统

邓赵红 张江滨 蒋亦樟 史荧中 王士同

邓赵红, 张江滨, 蒋亦樟, 史荧中, 王士同. 基于模糊子空间聚类的〇阶L2型TSK模糊系统[J]. 电子与信息学报, 2015, 37(9): 2082-2088. doi: 10.11999/JEIT150074
引用本文: 邓赵红, 张江滨, 蒋亦樟, 史荧中, 王士同. 基于模糊子空间聚类的〇阶L2型TSK模糊系统[J]. 电子与信息学报, 2015, 37(9): 2082-2088. doi: 10.11999/JEIT150074
Deng Zhao-hong, Zhang Jiang-bin, Jiang Yi-zhang, Shi Ying-zhong, Wang Shi-tong. Fuzzy Subspace Clustering Based Zero-order L2-norm TSK Fuzzy System[J]. Journal of Electronics & Information Technology, 2015, 37(9): 2082-2088. doi: 10.11999/JEIT150074
Citation: Deng Zhao-hong, Zhang Jiang-bin, Jiang Yi-zhang, Shi Ying-zhong, Wang Shi-tong. Fuzzy Subspace Clustering Based Zero-order L2-norm TSK Fuzzy System[J]. Journal of Electronics & Information Technology, 2015, 37(9): 2082-2088. doi: 10.11999/JEIT150074

基于模糊子空间聚类的〇阶L2型TSK模糊系统

doi: 10.11999/JEIT150074
基金项目: 

国家自然科学基金(61170122),江苏省杰出青年基金(BK20140001)和新世纪优秀人才支持计划(NCET120882)

Fuzzy Subspace Clustering Based Zero-order L2-norm TSK Fuzzy System

  • 摘要: 经典数据驱动型TSK(Takagi-Sugeno-Kang)模糊系统在获取模糊规则时,会考虑数据的所有特征空间,其带来一个重要缺陷:如果数据的特征空间维数过高,则系统获取的模糊规则繁杂,使系统复杂度增加而导致解释性下降。该文针对此缺陷,探讨了一种基于模糊子空间聚类的〇阶L2型TSK模糊系统(Fuzzy Subspace Clustering based zero-order L2- norm TSK Fuzzy System, FSC-0-L2-TSK-FS)构建新方法。新方法构建的模糊系统不仅能缩减模糊规则前件的特征空间,而且获取的模糊规则可对应于不同的特征子空间,从而具有更接近人类思维的推理机制。模拟和真实数据集上的建模结果表明,新方法增强了面对高维数据所建模型的解释性,同时所建模型得到了较之于一些经典方法更好或可比较的泛化性能。
  • Zadeh L A. Fuzzy sets[J]. Information and Control, 1965, 8(3): 338-353.
    李奕, 吴小俊. 基于监督学习的Takagi Sugeno Kang模糊系统图像融合方法研究[J]. 电子与信息学报, 2014, 36(5): 1126-1132.
    Li Yi and Wu Xiao-jun. A novel image fusion method using the Takagi Sugeno Kang fuzzy system based on supervised learning[J]. Journal of Electronics Information Technology, 2014, 36(5): 1126-1132.
    宋恒, 王晨, 马时平, 等. 基于非单点模糊支持向量机的判决反馈均衡器[J]. 电子与信息学报, 2008, 30(1): 117-120.
    Song Heng, Wang Chen, Ma Shi-ping, et al.. A decision feedback equalizer based on non-singleton fuzzy support vector machine[J]. Journal of Electronics Information Technology, 2008, 30(1): 117-120.
    Lughofer E. On-line assurance of interpretability criteria in evolving fuzzy systemsachievements, new concepts and open issues[J]. Information Sciences, 2013(251): 22-46.
    Riid A and Rstern E. Adaptability, interpretability and rule weights in fuzzy rule-based systems[J]. Information Sciences, 2014(257): 301-312.
    Thong N T and Son L H. HIFCF: an effective hybrid model between picture fuzzy clustering and intuitionistic fuzzy recommender systems for medical diagnosis[J]. Expert System with Application, 2015, 42(7): 3682-3701.
    Sanz J A, Galar M, Jurio A, et al.. Medical diagnosis of cardiovascular diseases using an interval-valued fuzzy rule-based classification system[J]. Applied Soft Computing, 2014(20): 103-111.
    Takagi T and Sugeno M. Fuzzy identification of systems and its applications to modeling and control[J]. IEEE Transactions on Systems, Man and Cybernetics, 1985(1): 116-132.
    Sugeno M and Kang G T. Structure identification of fuzzy model[J]. Fuzzy Sets and Systems, 1988, 28(1): 15-33.
    Jiang Yi-zhang, Chung Fu-lai, Ishibuchi H, et al.. Multitask TSK fuzzy system modeling by mining intertask common hidden structure[J]. IEEE Transactions on Cybernetics, 2015, 45(3): 548-561.
    Fadali S and Jafarzadeh S. TSK observers for discrete type-1 and type-2 fuzzy systems[J]. IEEE Transactions on Fuzzy Systems, 2014, 22(2): 451-458.
    Chung Fu-lai, Deng Zhao-hong, and Wang Shi-tong. From minimum enclosing ball to fast fuzzy inference system training on large datasets[J]. IEEE Transactions on Fuzzy Systems, 2009, 17(1): 173-184.
    Mamdani E H. Application of fuzzy logic to approximate reasoning using linguistic synthesis[J]. IEEE Transactions on Computers, 1977, 100(12): 1182-1191.
    Azeem M F, Hanmandlu M, and Ahmad N. Generalization of adaptive neuro-fuzzy inference systems[J]. IEEE Transactions on Neural Networks, 2000, 11(6): 1332-1346.
    Gan Guo-jun and Wu Jian-hong. A convergence theorem for the fuzzy subspace clustering (FSC) algorithm[J]. Pattern Recognition, 2008, 41(6): 1939-1947.
    Deng Zhao-hong, Choi Kup-sze, Chung Fu-lai, et al.. Enhanced soft subspace clustering integrating within-cluster and between-cluster information[J]. Pattern Recognition, 2010, 43(3): 767-781.
    Leski J M. TSK-fuzzy modeling based on -insensitive learning[J]. IEEE Transactions on Fuzzy Systems, 2005, 13(2): 181-193.
    Deng Zhao-hong, Choi Kup-sze, Chung Fu-lai, et al.. Scalable TSK fuzzy modeling for very large datasets using minimal- enclosing-ball approximation[J]. IEEE Transactions on Fuzzy Systems, 2011, 19(2): 210-226.
    Juang Chia-feng and Chiang Loa. Zero-order TSK-type fuzzy system learning using a two-phase swarm intelligence algorithm[J]. Fuzzy Sets and Systems, 2008, 159(21): 2910-2926.
    Tsang I W, Kwok J T Y, and Zurada J M. Generalized core vector machines[J]. IEEE Transactions on Neural Networks, 2006, 17(5): 1126-1140.
  • 加载中
计量
  • 文章访问数:  1304
  • HTML全文浏览量:  125
  • PDF下载量:  475
  • 被引次数: 0
出版历程
  • 收稿日期:  2015-01-13
  • 修回日期:  2015-05-11
  • 刊出日期:  2015-09-19

目录

    /

    返回文章
    返回