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

LIN Defu; WANG Jun; JIANG Yizhang; WANG Shitong

doi:10.11999/JEIT170792

Volume 40 Issue 6

May 2018

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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

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

doi: 10.11999/JEIT170792

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)

Received Date: 2017-08-07
Rev Recd Date: 2018-03-27
Publish Date: 2018-06-19

Abstract

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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References(18)

References

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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