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面向高維數(shù)據(jù)的Takagi-Sugeno模糊系統(tǒng)建模新方法

林得富 王駿 蔣亦樟 王士同

林得富, 王駿, 蔣亦樟, 王士同. 面向高維數(shù)據(jù)的Takagi-Sugeno模糊系統(tǒng)建模新方法[J]. 電子與信息學(xué)報(bào), 2018, 40(6): 1404-1411. doi: 10.11999/JEIT170792
引用本文: 林得富, 王駿, 蔣亦樟, 王士同. 面向高維數(shù)據(jù)的Takagi-Sugeno模糊系統(tǒng)建模新方法[J]. 電子與信息學(xué)報(bào), 2018, 40(6): 1404-1411. doi: 10.11999/JEIT170792
LIN Defu, WANG Jun, JIANG Yizhang, WANG Shitong. A Novel Takagi-Sugeno Fuzzy Systems Modeling Method for High Dimensional Data[J]. Journal of Electronics & Information Technology, 2018, 40(6): 1404-1411. doi: 10.11999/JEIT170792
Citation: LIN Defu, WANG Jun, JIANG Yizhang, WANG Shitong. A Novel Takagi-Sugeno Fuzzy Systems Modeling Method for High Dimensional Data[J]. Journal of Electronics & Information Technology, 2018, 40(6): 1404-1411. doi: 10.11999/JEIT170792

面向高維數(shù)據(jù)的Takagi-Sugeno模糊系統(tǒng)建模新方法

doi: 10.11999/JEIT170792
基金項(xiàng)目: 

國家自然科學(xué)基金(61300151),江蘇省自然科學(xué)基金(BK20160187, BK20161268),中央高?;究蒲袠I(yè)務(wù)費(fèi)專項(xiàng)項(xiàng)目(JUSRP11737)

A Novel Takagi-Sugeno Fuzzy Systems Modeling Method for High Dimensional Data

Funds: 

The National Natural Science Foundation of China (61300151), The Natural Science Foundation of Jiangsu Province (BK20160187, BK20161268), The Fundamental Research Funds for the Central Universities (JUSRP11737)

  • 摘要: 對高維數(shù)據(jù)進(jìn)行建模是Takagi-Sugeno(T-S)模糊系統(tǒng)建模面臨的一個重大挑戰(zhàn)。為此,該文提出一種特征選擇與組稀疏編碼相結(jié)合的模糊系統(tǒng)建模新方法WOMP-GS-FIS。首先,運(yùn)用一種新型的加權(quán)正交匹配追蹤算法對原始樣本進(jìn)行特征選擇,在此基礎(chǔ)上提取出模糊規(guī)則前件并產(chǎn)生模糊系統(tǒng)字典;然后,基于組稀疏正則化構(gòu)造關(guān)于后件參數(shù)的組稀疏優(yōu)化問題,在優(yōu)化問題求解的同時得到重要的模糊規(guī)則。實(shí)驗(yàn)結(jié)果表明,在保證模型泛化性能的前提下,該方法不僅能對所獲得的模糊規(guī)則結(jié)構(gòu)進(jìn)行精簡還可以進(jìn)一步減少模糊規(guī)則數(shù),進(jìn)而解決高維數(shù)據(jù)環(huán)境下模糊規(guī)則可解釋性差的問題。
  • 程旸, 顧曉清, 蔣亦樟, 等. 具備視角協(xié)同學(xué)習(xí)能力的多視角TSK型模糊系統(tǒng)[J]. 電子與信息學(xué)報(bào), 2016, 38(8): 2054-2061. doi: 10.11999/JEIT151209.
    FERNNDEZ A, CARMONA C J, JESUS M J D, et al. A view on fuzzy systems for big data: Progress and opportunities[J]. International Journal of Computational Intelligence Systems, 2016, 9(s1): 69-80. doi: 10.1080/ 18756891.2016.1180820.
    CHENG Yang, GU Xiaoqing, JIANG Yizhang, et al. Multi- view TSK fuzzy system via collaborative learning[J]. Journal of Electronics Information Technology, 2016, 38(8): 2054-2061. doi: 10.11999/JEIT151209.
    LUO Minnan, SUN Fuchun, and LIU Huaping. Hierarchical structured sparse representation for T-S fuzzy systems identification[J]. IEEE Transactions on Fuzzy Systems, 2013, 21(6): 1032-1043. doi: 10.1109/TFUZZ.2013.2240690.
    JIANG Yizhang, DENG Zhaohong, CHUNG Fulai, et al. Recognition of epileptic EEG signals using a novel multiview TSK fuzzy system[J]. IEEE Transactions on Fuzzy Systems, 2017, 25(1): 3-20. doi: 10.1109/TFUZZ.2016.2637405.
    LUO Minnan, SUN Fuchun, and LIU Huaping. Joint block structure sparse representation for Multi-Input-Multi-Output (MIMO) T-S fuzzy system identification[J]. IEEE Transactions on Fuzzy Systems, 2014, 22(6): 1387-1400. doi: 10.1109/TFUZZ.2013.2292973.
    JUANG Chiafeng and HSIEH C D. TS-fuzzy system-based support vector regression[J]. Fuzzy Sets Systems, 2009, 160(17): 2486-2504. doi: 10.1016/j.fss.2008.11.022.
    JUANG Chiafeng and CHEN Guocyuan. A TS fuzzy system learned through a support vector machine in principal component space for real-time object detection[J]. IEEE Transactions on Industrial Electronics, 2012, 59(8): 3309-3320. doi: 10.1109/TIE.2011.2159949.
    羅敏楠. T-S模糊推理系統(tǒng)的結(jié)構(gòu)稀疏編碼辨識理論與方法[D]. [博士論文], 清華大學(xué), 2014: 1-26.
    LUO Minnan. Theory and approches of T-S fuzzy inference systems identification with structure sparse coding[D]. [Ph.D. dissertation], Tsinghua University, 2014: 1-26.
    LUGHOFER E and KINDERMANN S. SparseFIS: data- driven learning of fuzzy systems with sparsity constraints[J]. IEEE Transactions on Fuzzy Systems, 2010, 18(2): 396-411. doi: 10.1109/TFUZZ.2010.2042960.
    SANA F, KATTERBAUER K, AL-NAFFOURI T Y, et al. Orthogonal matching pursuit for enhanced recovery of sparse geological structures with the ensemble kalman filter[J]. IEEE Journal of Selected Topics in Applied Earth Observations Remote Sensing, 2016, 9(4): 1710-1724. doi: 10.1109/JSTARS.2016.2518119.
    ZHOU Dengyong, BOUSQUET O, LAL T N, et al. Learning with local and global consistency[C]. Advances in Neural Information Processing Systems, Vancouver, Canada, 2004: 321-328.
    RODRGUEZ-FDEZ I, MUCIENTES M, and BUGARN A. Fruler: Fuzzy rule learning through evolution for regression [J]. Information Sciences, 2016, 354: 1-18. doi: 10.1016/j.ins. 2016.03.012.
    YUAN Ming and LIN Yi. Model selection and estimation in regression with grouped variables[J]. Journal of the Royal Statistical Society, 2006, 68(1): 49-67. doi: 10.1111/j.1467- 9868.2005.00532.x.
    ZHANG Caiya and XIANG Yanbiao. On the oracle property of adaptive group lasso in high-dimensional linear models[J]. Statistical Papers, 2016, 57(1): 249-265. doi: 10.1007/s00362- 015-0684-0.
    GRIGORIE L T and BOTEZ R M. Adaptive neuro-fuzzy inference system-based controllers for smart material actuator modelling[J]. Proceedings of the Institution of Mechanical Engineers Part G Journal of Aerospace Engineering, 2009, 223(6): 655-668. doi: 10.1243/09544100 JAERO522.
    NOROUZI J, YADOLLAHPOUR A, MIRBAGHERI S A, et al. Predicting renal failure progression in chronic kidney disease using integrated intelligent fuzzy expert system[J]. Computational Mathematical Methods in Medicine, 2016, 2016(3): 1-9. doi: 10.1155/2016/6080814.
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出版歷程
  • 收稿日期:  2017-08-07
  • 修回日期:  2018-03-27
  • 刊出日期:  2018-06-19

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