This paper proposes a novel gradient-boosting-based ensemble system with a fuzzy
regression tree (FRT) as its base component for regression tasks. FRT first initializes the rule …

The data-driven approach is popular to automate learning of fuzzy rules and tuning
membership function parameters in fuzzy inference systems (FIS) development. However …

Existing research mainly uses prior knowledge to set all fuzzy sets in a fuzzy system to the
same type. In data-driven fuzzy modeling, automatically determining the type of fuzzy set is …

In this paper, a new method of hierarchical fuzzy system modeling for high-dimensional
regression problems is proposed, which is called multi-objective evolutionary hierarchical …

Multi-objective evolutionary algorithms based on decomposition (MOEA/Ds) have been
generally recognized as competitive techniques for solving multi-objective optimization …

Fuzzy neural networks (FNNs) have gained attention for their interpretability and self-
learning ability. However, they struggle with interpreting high-dimensional unstructured data …

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 …

One of the most important factors affecting the interpretability of Fuzzy Rule-Based Systems
(FRBSs) is the number of features used Indeed, the employment of a large number of …

Mobile robot navigation is an essential problem in robotics. We propose a method for
constructing and training fuzzy logic controllers (FLCs) to coordinate a robotic team …

Time series forecasting is an essential and challenging task, especially for large-scale time-
series (LSTS) forecasting, which plays a crucial role in many real-world applications. Due to …