多任务模糊聚类驱动的多任务TSK模糊系统模型

doi:10.3969/j.issn.0255-8297.2020.05.007

Abstract

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.

Key words: multi-task learning, multi-task fuzzy c-means, multi-task collaborative learning, multi-task Takagi-Sugeno-Kang fuzzy systems

CLC Number:

P751.1

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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Multi-task Fuzzy Clustering Based Multi-task TSK Fuzzy System

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