A novel multi-task TSK fuzzy system modeling method based on multi-task fuzzy clustering

Article type: Research Article

Authors: Yao, Ziyang^{; *}

Affiliations: The School of Internet of Things and Artificial Intelligence, Wuxi Vocational College of Science and Technology, Wuxi, P.R. China

Correspondence: [*] Corresponding author. Ziyang Yao, The School of Internet of Things and Artificial Intelligence, Wuxi Vocational College of Science and Technology, Wuxi, P.R. China. E-mail: [email protected].

Abstract: The traditional multi-task Takagi-Sugeno-Kang (TSK) fuzzy system modeling methods pay more attention to utilizing the inter-task correlation to learn the consequent parameters but ignore the importance of the antecedent parameters of the model. To this end, we propose a novel multi-task TSK fuzzy system modeling method based on multi-task fuzzy clustering. This method first proposes a novel multi-task fuzzy c-means clustering method that learns multiple specific clustering centers for each task and some common clustering centers for all tasks. Secondly, for the consequent parameters of the fuzzy system, the novel low-rank and row-sparse constraints are proposed to better implement multi-task learning. The experimental results demonstrate that the proposed model shows better performance compared with other existing methods.

Keywords: Multi-task fuzzy clustering, TSK fuzzy system, low-rank, row-sparsity, joint learning

DOI: 10.3233/JIFS-232312

Journal: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2024