Evolving fuzzy systems (EFSs) are widely known as a powerful tool for streaming data
prediction. In this article, a novel zero-order EFS with a unique belief structure is proposed …

Abstract The Metacognitive Scaffolding Learning Machine (McSLM), combining the concept
of metacognition—what-to-learn, how-to-learn, and when-to-learn, and the Scaffolding …

Dealing with uncertain data requires effective methods to properly describe their real
meaning in terms of a tradeoff between interpretability and generality on the process of …

During the last two decades, evolving fuzzy systems (EFSs) have attracted more and more
attention owing to their capacity to self-adapt both system structures and parameters …

This paper presents the unification and generalization of different evolving clustering
methods based on Cauchy density. This can be done by introducing different inner matrix …

Evolving fuzzy systems (EFSs) are now well developed and widely used, thanks to their
ability to self-adapt both their structures and parameters online. Since the concept was first …

In this work, the Kalman Filter (KF) and Takagi–Sugeno fuzzy modeling technique are
combined to extend the classical Kalman linear state estimation to the nonlinear field. The …

This paper proposes a method and an algorithm to implement interpretable fuzzy
reinforcement learning (IFRL). It provides alternative solutions to common problems in RL …

In this paper, we propose a new approach to fuzzy data clustering. We present a new
algorithm, called TEDA-Cloud, based on the recently introduced TEDA approach to outlier …

In this paper, a novel fully data-driven algorithm, named Self-Organised Direction Aware
(SODA) data partitioning and forming data clouds is proposed. The proposed SODA …