This article presents a novel method for fuzzy space partitioning and the identification of
Takagi-Sugeno fuzzy models. The novelty is in its region-splitting mechanism and …

The motivation behind 15 years of continuous development within the topic of the tensor
product (TP) model transformation is that the greater the number of parameters or …

Existing Takagi-Sugeno-Kang (TSK) fuzzy models proposed in the literature attempt to
optimize the global learning accuracy as well as to maintain the interpretability of the local …

This paper proposes a new type-2 fuzzy c-regression clustering algorithm for the structure
identification phase of Takagi–Sugeno (T–S) systems. We present uncertainties with fuzzifier …

While interpretable antecedent parts of first-order Takagi–Sugeno–Kang (TSK) fuzzy rules
can be properly acquired by adopting some clustering methods, this study aims at avoiding …

A recurrent neuro-fuzzy approach with RO-LSE hybrid learning algorithm to the problem of
system modeling is proposed in the paper. The proposed recurrent neuro-fuzzy system …

Wang–Mendel (WM) fuzzy system is an effective and interpretable model for solving tabular
data classification problem. However, original WM fuzzy system is weak in handling dataset …

This paper proposes a new incremental learning approach to endow a Takagi-Sugeno-type
fuzzy classification model with high generalization ability. The proposed fuzzy model is …

Multi-task learning aims to explore task relatedness to improve individual tasks, which is of
particular significance in the challenging scenario that only limited data is available for each …

In order to improve the performance of the fuzzy clustering algorithm in fuzzy space partition
in the identification of the Takagi-Sugeno (TS) fuzzy model, a hyperplane prototype fuzzy …