[1]赵壮壮,王骏,潘祥,等.任务间共享和特有结构分解的多任务TSK模糊系统建模[J].智能系统学报,2021,16(4):622-629.[doi:10.11992/tis.202007009]
 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]
点击复制

任务间共享和特有结构分解的多任务TSK模糊系统建模(/HTML)
分享到:

《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第16卷
期数:
2021年4期
页码:
622-629
栏目:
学术论文—机器学习
出版日期:
2021-07-05

文章信息/Info

Title:
Multi-task TSK fuzzy system modeling based on inter-task common and special structure decomposition
作者:
赵壮壮1 王骏2 潘祥1 邓赵红1 施俊2 王士同1
1. 江南大学 人工智能与计算机学院,江苏 无锡 214122;
2. 上海大学 通信与信息工程学院,上海 200444
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
关键词:
TSK模糊系统非线性多任务低秩稀疏参数分解泛化性能可解释性
Keywords:
TSK fuzzy systemnonlinearmultitasklow-ranksparseparameter decompositiongeneralization performanceinterpretability
分类号:
TP181
DOI:
10.11992/tis.202007009
摘要:
现有的多任务Takagi-Sugeno-Kang (TSK) 模糊建模方法更注重利用任务间的相关性信息,而忽略了单个任务的特殊性。针对此问题,本文提出了一种考虑所有任务之间的共享结构和特有结构的TSK模糊系统多任务建模新方法。该方法将后件参数分解为共享参数和特有参数两个分量,既充分利用了任务间共享信息,又有效地保留了单个任务的特性。最后,本文利用增广拉格朗日乘子法(ALM)求解该最优化问题。实验结果表明,该方法比现有的模型获得了更好的表现。
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.

参考文献/References:

[1] LI Chaoshun, ZHOU Jianzhong, CHANG Li, et al. T-S fuzzy model identification based on a novel hyperplane-shaped membership function[J]. IEEE transactions on fuzzy systems, 2017, 25(5): 1364-1370.
[2] XU Peng, DENG Zhaohong, CUI Chen, et al. Concise fuzzy system modeling integrating soft subspace clustering and sparse learning[J]. IEEE transactions on fuzzy systems, 2019, 27(11): 2176-2189.
[3] CHANG P C, LIU Chenhao. A TSK type fuzzy rule based system for stock price prediction[J]. Expert systems with applications, 2008, 34(1): 135-144.
[4] ZHOU Shangming, GAN J Q. Extracting Takagi-Sugeno fuzzy rules with interpretable submodels via regularization of linguistic modifiers[J]. IEEE transactions on knowledge and data engineering, 2009, 21(8): 1191-1204.
[5] DENG Zhaohong, CHOI K S, CHUNG F L, 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.
[6] JUANG C F, HSIEH C D. TS-fuzzy system-based support vector regression[J]. Fuzzy sets and systems, 2009, 160(17): 2486-2504.
[7] 林得富, 王骏, 张嘉旭, 等. Takagi-Sugeno模糊系统双正则联合稀疏建模新方法[J]. 计算机科学与探索, 2019, 13(6): 1016-1026
LIN Defu, WANG Jun, ZHANG Jiaxu, et al. Novel Takagi-Sugeno fuzzy system modeling method via joint sparse learning using two regularizations[J]. Journal of frontiers of computer science and technology, 2019, 13(6): 1016-1026
[8] 张春香, 王骏, 张嘉旭, 等. 面向自闭症辅助诊断的联合组稀疏TSK建模方法[J]. 计算机科学与探索, 2020, 14(2): 2083-2093
ZHANG Chunxiang, WANG Jun, ZHANG Jiaxu, et al. Novel TSK modeling method with joint group sparse learning for autism aided diagnosis[J]. Journal of frontiers of computer science and technology, 2020, 14(2): 2083-2093
[9] ZHU Yuanguo. Fuzzy optimal control for multistage fuzzy systems[J]. IEEE transactions on systems, man, and cybernetics, part B (cybernetics), 2011, 41(4): 964-975.
[10] JANG J S R. ANFIS: adaptive-network-based fuzzy inference system[J]. IEEE transactions on systems, man, and cybernetics, 1993, 23(3): 665-685.
[11] REZAEE B, ZARANDI M H F. Data-driven fuzzy modeling for Takagi-Sugeno-Kang fuzzy system[J]. Information sciences, 2010, 180(2): 241-255.
[12] CAVALLANTI G, CESA-BIANCHI N, GENTILE C. Linear algorithms for online multitask classification[J]. Journal of machine learning research, 2010, 11(5): 2901-2934.
[13] EVGENIOU T, MICCHELLI C A, PONTIL M. Learning multiple tasks with kernel methods[J]. Journal of machine learning research, 2005, 6(4): 615-637.
[14] ZHANG Jiangmei, YU Binfeng, JI Haibo, et al. Multi-task feature learning by using trace norm regularization[J]. Open physics, 2017, 15(1): 674-681.
[15] ZHAO Qian, RUI Xiangyu, HAN Zhi, et al. Multilinear multitask learning by rank-product regularization[J]. IEEE transactions on neural networks and learning systems, 2020, 31(4): 1336-1350.
[16] ZHONG Shi, PU Jian, JIANG Yugang, et al. Flexible multi-task learning with latent task grouping[J]. Neurocomputing, 2016, 189: 179-188.
[17] XUE Ya, LIAO Xuejun, CARIN L, et al. Multi-task learning for classification with dirichlet process priors[J]. Journal of machine learning research, 2007, 8: 35-63.
[18] JIANG Yizhang, DENG Zhaohong, CHUNG F L, et al. Multi-task TSK fuzzy system modeling using inter-task correlation information[J]. Information sciences, 2015, 298: 512-533.
[19] WANG Jun, LIN Defu, DENG Zhaohong, et al. Multitask TSK fuzzy system modeling by jointly reducing rules and consequent parameters[J]. IEEE transactions on systems, man, and cybernetics: systems, 2019.
[20] MENG Fan, YANG Xiaomei, ZHOU Chenghu, et al. The augmented lagrange multipliers method for matrix completion from corrupted samplings with application to mixed gaussian-impulse noise removal[J]. PLoS one, 2014, 9(9): e108125.
[21] LIN Zhouchen, LIU Risheng, SU Zhixun. Linearized alternating direction method with adaptive penalty for low-rank representation[C]//Proceedings of 25th Annual Conference on Neural Information Processing Systems. Granada, Spain, 2011: 612-620.
[22] CAI Jianfeng, CANDèS E J, SHEN Zuowei. A singular value thresholding algorithm for matrix completion[J]. SIAM journal on optimization, 2010, 20(4): 1956-1982.
[23] LIU Guangcan, LIU Zhouchen, YAN Shuicheng. Robust recovery of subspace structures by low-rank representation[J]. IEEE transactions on pattern analysis and machine intelligence, 2013, 35(1): 171-184.
[24] JIANG Pengbo, WANG Xuetong, LI Qiongling, et al. Correlation-aware sparse and low-rank constrained multi-task learning for longitudinal analysis of alzheimer’s disease[J]. IEEE journal of biomedical and health informatics, 2019, 23(4): 1450-1456.
[25] ZHOU Jiayu, CHEN Jianhui, YE Jieping. Malsar: multi-task learning via structural regularization—user’s manual version 1.1[EB/OL]. (2019-12-12) [2020-07-08] https://github.com/jiayuzhou/MALSAR.
[26] JUANG C F, CHIU S H, SHIU S J. Fuzzy system learned through fuzzy clustering and support vector machine for human skin color segmentation[J]. IEEE transactions on systems, man, and cybernetics-part A: systems and humans, 2007, 37(6): 1077-1087.

相似文献/References:

[1]陈增强,黄朝阳,孙明玮,等.基于大变异遗传算法进行参数优化整定的负荷频率自抗扰控制[J].智能系统学报,2020,15(1):41.[doi:10.11992/tis.201906026]
 CHEN Zengqiang,HUANG Zhaoyang,SUN Mingwei,et al.Active disturbance rejection control of load frequency based on big probability variation’s genetic algorithm for parameter optimization[J].CAAI Transactions on Intelligent Systems,2020,15(4):41.[doi:10.11992/tis.201906026]
[2]郭宪,方勇纯.仿生机器人运动步态控制:强化学习方法综述[J].智能系统学报,2020,15(1):152.[doi:10.11992/tis.201907052]
 GUO Xian,FANG Yongchun.Locomotion gait control for bionic robots: a review of reinforcement learning methods[J].CAAI Transactions on Intelligent Systems,2020,15(4):152.[doi:10.11992/tis.201907052]
[3]宋锐,方勇纯,刘辉.基于LiDAR/INS的野外移动机器人组合导航方法[J].智能系统学报,2020,15(4):804.[doi:10.11992/tis.202008026]
 SONG Rui,FANG Yongchun,LIU Hui.Integrated navigation approach for the field mobile robot based on LiDAR/INS[J].CAAI Transactions on Intelligent Systems,2020,15(4):804.[doi:10.11992/tis.202008026]
[4]邹守睿,武毅男,方勇纯.循环神经网络前馈补偿的压电驱动器跟踪控制[J].智能系统学报,2021,16(3):567.[doi:10.11992/tis.202104010]
 ZOU Shourui,WU Yinan,FANG Yongchun.Tracking control of piezoelectric actuator based on feedforward compensation of recurrent neural network[J].CAAI Transactions on Intelligent Systems,2021,16(4):567.[doi:10.11992/tis.202104010]

备注/Memo

备注/Memo:
收稿日期:2020-07-06。
基金项目:江苏省自然科学基金项目(BK20181339);国家自然科学基金项目(61602007);中央高校基础研究经费资助项目(JUSRP11851)
作者简介:赵壮壮,硕士研究生,主要研究方向为模式识别与人工智能、模糊系统;王骏,副教授,主要研究方向为机器学习、模糊系统、医学影像分析;潘祥,副教授、主要研究方向为医学图像诊断、计算机视觉、AI医疗诊断。主持国家自然科学基金项目1项,安徽省自然科学基金项目1项。获得授权发明专利6项,受理发明专利2项。发表学术论文20余篇
通讯作者:王骏.E-mail:wangjun_shu@shu.edu.cn
更新日期/Last Update: 1900-01-01