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

Deng Zhao-hong; Zhang Jiang-bin; Jiang Yi-zhang; Shi Ying-zhong; Wang Shi-tong

doi:10.11999/JEIT150074

Volume 37 Issue 9

Sep. 2015

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2015 > 37(9): 2082-2088

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

Citation:

PDF( 418 KB)

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

doi: 10.11999/JEIT150074

Received Date: 2015-01-13
Rev Recd Date: 2015-05-11
Publish Date: 2015-09-19

Abstract

Abstract

The classical data driven Takagi-Sugeno-Kang (TSK) fuzzy system considers all the features of trained data, and faces a challenge that the interpretation is degenerated and the obtained fuzzy rule is complex when trained by high dimensional data. In this paper, a new fuzzy model, i.e., Fuzzy Subspace Clustering based zero-order L2-norm TSK Fuzzy System (FSC-0-L2-TSK-FS) is proposed to overcome this difficulty. The proposed fuzzy system not only reduces the feature spaces of the rule of antecedent, but also makes different rules implement the inference in different subspaces. The inference mechanism of the proposed fuzzy model training algorithm is very similar to the inference procedure of human. The experimental studies on the synthetic and real datasets prove that the interpretation of model constructed by the proposed method is enhanced when trained by high dimensional data and the generalization performance is better or comparative to several classical TSK fuzzy systems training methods.
- Takagi-Sugeno-Kang (TSK) fuzzy system,
- Medical diagnosis,
- Interpretability,
- High-dimensional data

FullText(HTML)

References(22)

References

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.

Relative Articles

Supplements(0)

Cited By

Proportional views