[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]
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任务间共享和特有结构分解的多任务TSK模糊系统建模

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.
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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

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