[1]ZHAO Zhuangzhuang,WANG Jun,PAN Xiang,et al.Multi-task TSK fuzzy system modeling based on inter-task common and special structure decomposition[J].CAAI Transactions on Intelligent Systems,2021,16(4):622-629.[doi:10.11992/tis.202007009]
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Multi-task TSK fuzzy system modeling based on inter-task common and special structure decomposition

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 16 Number of periods: 2021 4 Page number: 622-629 Column: 学术论文—机器学习 Public date: 2021-07-05
Title:
Multi-task TSK fuzzy system modeling based on inter-task common and special structure decomposition
Author(s):
ZHAO Zhuangzhuang1;  WANG Jun2;  PAN Xiang1;  DENG Zhaohong1;  SHI Jun2;  WANG Shitong1
1. School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China;
2. School of Communication & Information Engineering, Shanghai University, Shanghai 200444, China
Keywords:
TSK fuzzy system; nonlinear; multitask; low-rank; sparse; parameter decomposition; generalization performance; interpretability
CLC:
TP181
DOI:
10.11992/tis.202007009
Abstract:
Existing Takagi–Sugeno–Kang (TSK) fuzzy system modeling methods pay more attention to the inter-task correlation but ignore the particularity of every single task. To address this issue, this paper proposes a novel multi-task modeling method for TSK fuzzy systems taking common and specific structures across all tasks (MTTSKFS-CS) into consideration. This method decomposes consequent parameters into shared and special ones, which not only takes advantage of the shared information among tasks but also effectively preserves the characteristics of individual tasks. Finally, the study uses the augmented Lagrange multiplier for optimization. The experimental results demonstrate the better performance of the proposed model compared with other existing methods.
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