| 多任务模糊聚类驱动的多任务TSK模糊系统模型 |
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| 引用本文： | 蒋亦樟,华蕾,张群,钱鹏江,夏开建.多任务模糊聚类驱动的多任务TSK模糊系统模型[J].应用科学学报,2020,38(5):742-760. |
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| 作者姓名： | 蒋亦樟 华蕾 张群 钱鹏江 夏开建 |
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| 作者单位： | 1. 江南大学 人工智能与计算机学院, 江苏 无锡 214122;2. 江南大学 图书馆, 江苏 无锡 214122;3. 苏州大学附属常熟医院 信息科, 江苏 苏州 215500;4. 中国矿业大学 信息与控制工程学院, 江苏 徐州 221116 |
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| 基金项目： | 国家自然科学基金（No.61702225，No.61772241）；国家社科基金一般项目（No.19BTQ030）；江苏省六大人才高峰高层次人才计划（No.XYDXX-127）；无锡市社会发展项目（No.WX18IVJN002）；江苏省卫健委面上项目（No.2018071）资助 |
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| 摘 要： | 该文提出了一种多任务Takagi-Sugeno-Kang（TSK）模糊系统建模方法.首先给出了一种新的多任务模糊c均值聚类算法，能够有效提取所有任务之间的公共信息和每个任务的私有信息，进而利用所得的聚类中心构建多任务TSK模糊系统的前件参数.其次设计了一种具备多任务协同学习机制的后件参数优化方法，可以优化多任务TSK模糊系统的后件参数.最后基于优化的前后件参数，构建出具体多任务模糊聚类方法驱动的多任务TSK模糊系统模型（multi-task fuzzy c-means based multi-task TSK fuzzy system，MTFCM-MT-TSK-FS）以用于实际应用.分别在合成和真实数据集上进行实验，结果验证了该模型的有效性.
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| 关 键 词： | 多任务学习 多任务模糊c均值 多任务协同学习 多任务Takagi-Sugeno-Kang模糊系统式 |
| 收稿时间： | 2020-03-31 |
Multi-task Fuzzy Clustering Based Multi-task TSK Fuzzy System |
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| JIANG Yizhang,HUA Lei,ZHANG Qun,QIAN Pengjiang,XIA Kaijian.Multi-task Fuzzy Clustering Based Multi-task TSK Fuzzy System[J].Journal of Applied Sciences,2020,38(5):742-760. |
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| Authors: | JIANG Yizhang HUA Lei ZHANG Qun QIAN Pengjiang XIA Kaijian |
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| Institution: | 1. School of Artificial Intelligence and Computer, Jiangnan University, Wuxi 214122, Jiangsu, China;2. Library of Jiangnan University, Wuxi 214122, Jiangsu, China;3. Department of Information, Changshu Hospital Affiliated to Soochow University, Suzhou 215500, Jiangsu, China;4. School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, Jiangsu, China |
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| Abstract: | In this paper, a novel modeling approach of multi-task Takagi-Sugeno-Kang (TSK) fuzzy system is presented. Firstly, we propose a new multi-task fuzzy c-means clustering algorithm, which is used to extract the public information between all tasks and the private information of each task effectively. Accordingly, the antecedent parameters of multi task TSK fuzzy system can be constructed with the obtained clustering centers. Secondly, a novel consequent parameters optimization method of the multi-task TSK fuzzy system is proposed based on the multi-task collaborative learning mechanism. Finally, a practical application oriented multi-task TSK fuzzy system is completed based on the two proposed algorithms. Experimental results on several synthetic and real-world datasets demonstrate the validity of the proposed model. |
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| Keywords: | multi-task learning multi-task fuzzy c-means multi-task collaborative learning multi-task Takagi-Sugeno-Kang fuzzy systems |
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