Multitask TSK fuzzy system modeling by mining intertask common hidden structure
Y Jiang, FL Chung, H Ishibuchi… - IEEE transactions on …, 2014 - ieeexplore.ieee.org
The classical fuzzy system modeling methods implicitly assume data generated from a
single task, which is essentially not in accordance with many practical scenarios where data …
single task, which is essentially not in accordance with many practical scenarios where data …
Multi-task TSK fuzzy system modeling using inter-task correlation information
Y Jiang, Z Deng, FL Chung, S Wang - Information Sciences, 2015 - Elsevier
The classical fuzzy system modeling methods have been typically developed for the single
task modeling scene, which is essentially not in accordance with many practical applications …
task modeling scene, which is essentially not in accordance with many practical applications …
Knowledge-leverage-based TSK fuzzy system modeling
Classical fuzzy system modeling methods consider only the current scene where the training
data are assumed to be fully collectable. However, if the data available from the current …
data are assumed to be fully collectable. However, if the data available from the current …
On the functional equivalence of TSK fuzzy systems to neural networks, mixture of experts, CART, and stacking ensemble regression
Fuzzy systems have achieved great success in numerous applications. However, there are
still many challenges in designing an optimal fuzzy system, eg, how to efficiently optimize its …
still many challenges in designing an optimal fuzzy system, eg, how to efficiently optimize its …
On modeling of data-driven monotone zero-order TSK fuzzy inference systems using a system identification framework
A system identification-based framework is used to develop monotone fuzzy If-Then rules for
formulating monotone zero-order Takagi-Sugeno-Kang (TSK) fuzzy inference systems (FISs) …
formulating monotone zero-order Takagi-Sugeno-Kang (TSK) fuzzy inference systems (FISs) …
Realizing two-view TSK fuzzy classification system by using collaborative learning
Y Jiang, Z Deng, FL Chung… - IEEE transactions on …, 2016 - ieeexplore.ieee.org
In this paper, a novel Takagi-Sugeno-Kang (TSK) fuzzy classification system (FCS) is firstly
presented for pattern classification tasks. It is distinguished by having the large margin …
presented for pattern classification tasks. It is distinguished by having the large margin …
Stacked-structure-based hierarchical Takagi-Sugeno-Kang fuzzy classification through feature augmentation
T Zhou, H Ishibuchi, S Wang - IEEE Transactions on Emerging …, 2017 - ieeexplore.ieee.org
In this paper, a new stacked-structure-based hierarchical Takagi-Sugeno-Kang (TSK) fuzzy
classifier called SHFA-TSK-FC with both promising performance and high interpretability is …
classifier called SHFA-TSK-FC with both promising performance and high interpretability is …
Improving the interpretability of TSK fuzzy models by combining global learning and local learning
The fuzzy inference system proposed by Takagi, Sugeno, and Kang, known as the TSK
model in fuzzy system literature, provides a powerful tool for modeling complex nonlinear …
model in fuzzy system literature, provides a powerful tool for modeling complex nonlinear …
Layer normalization for TSK fuzzy system optimization in regression problems
Recently, mini-batch gradient descent (MBGD)-based optimization has become popular in
Takagi–Sugeno–Kang (TSK) fuzzy system optimization. However, it suffers from some …
Takagi–Sugeno–Kang (TSK) fuzzy system optimization. However, it suffers from some …
Disjunctive fuzzy neural networks: A new splitting-based approach to designing a T–S fuzzy model
N Wang, W Pedrycz, W Yao, X Chen… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
This article proposes a new network approach toward the implementation of Takagi–Sugeno
(T–S) fuzzy models referred to as disjunctive fuzzy neural networks (DJFNNs). The proposed …
(T–S) fuzzy models referred to as disjunctive fuzzy neural networks (DJFNNs). The proposed …