A wide interpretable Gaussian Takagi–Sugeno–Kang fuzzy classifier and its incremental learning

作者：

Highlights：

• We design a wide interpretable TSK fuzzy classifier WIG-TSK which can simultaneously train all its sub-classifier without aggregation strategy.

• We provide the theoretical justifications for WIG-TSK about its enhanced generalization capability and structure equivalence.

• We develop the incremental version of WIG-TSK for online learning.

摘要

•We design a wide interpretable TSK fuzzy classifier WIG-TSK which can simultaneously train all its sub-classifier without aggregation strategy.•We provide the theoretical justifications for WIG-TSK about its enhanced generalization capability and structure equivalence.•We develop the incremental version of WIG-TSK for online learning.

论文关键词：Gaussian TSK fuzzy classifiers,Wide combination,Incremental algorithm,Interpretability,Generalization capability

论文评审过程：Received 28 October 2021, Revised 20 December 2021, Accepted 8 January 2022, Available online 18 January 2022, Version of Record 1 February 2022.

论文官网地址：https://doi.org/10.1016/j.knosys.2022.108203