面向高维数据的Takagi-Sugeno模糊系统建模新方法

林得富; 王骏; 蒋亦樟; 王士同

doi:10.11999/JEIT170792

面向高维数据的Takagi-Sugeno模糊系统建模新方法

doi: 10.11999/JEIT170792

基金项目:

国家自然科学基金(61300151)，江苏省自然科学基金(BK20160187, BK20161268),中央高校基本科研业务费专项项目(JUSRP11737)

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出版历程
- 收稿日期: 2017-08-07
- 修回日期: 2018-03-27
- 刊出日期: 2018-06-19

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

摘要: 对高维数据进行建模是Takagi-Sugeno(T-S)模糊系统建模面临的一个重大挑战。为此，该文提出一种特征选择与组稀疏编码相结合的模糊系统建模新方法WOMP-GS-FIS。首先，运用一种新型的加权正交匹配追踪算法对原始样本进行特征选择，在此基础上提取出模糊规则前件并产生模糊系统字典；然后，基于组稀疏正则化构造关于后件参数的组稀疏优化问题，在优化问题求解的同时得到重要的模糊规则。实验结果表明，在保证模型泛化性能的前提下，该方法不仅能对所获得的模糊规则结构进行精简还可以进一步减少模糊规则数，进而解决高维数据环境下模糊规则可解释性差的问题。
- T-S模糊系统建模 /
- 特征选择 /
- 组稀疏编码 /
- 精简规则结构 /
- 模糊规则约减
Abstract: It is a great challenge to model Takagi-Sugeno(T-S) fuzzy systems on high dimensional data due to the problem of the curse of dimensionality. To this end, a novel T-S fuzzy system modeling method called WOMP-GS-FIS is proposed. The proposed method considers feature selection and group sparse coding simultaneously. Specifically, feature selection is performed by a novel Weighted Orthogonal Matching Pursuit (WOMP) method, based on which the fuzzy rule antecedent part is extracted and the dictionary of the fuzzy system is generated. Then, a group sparse optimization problem based on the group sparse regularization is formulated to obtain the optimal consequent parameters. In this way, the major fuzzy rules are selected by utilizing the group information that existing in the T-S fuzzy systems. The experimental results show that the proposed method can not only simplify the rule,s structure, but also reduce the number of fuzzy rules under the premise of good generalization performance, so as to solve the poor interpretation problem of fuzzy rules on high dimensional data effectively.
- T-S fuzzy systems modeling /
- Feature selection /
- Group sparse coding /
- Simplify rule's structure /
- Fuzzy rules reduction

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参考文献(18)

程旸, 顾晓清, 蒋亦樟, 等. 具备视角协同学习能力的多视角TSK型模糊系统[J]. 电子与信息学报, 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.

罗敏楠. T-S模糊推理系统的结构稀疏编码辨识理论与方法[D]. [博士论文], 清华大学, 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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