Linguistic fuzzy modelling, developed by linguistic fuzzy rule-based systems, allows us to
deal with the modelling of systems by building a linguistic model which could become …

The need for trading off interpretability and accuracy is intrinsic to the use of fuzzy systems.
The obtaining of accurate but also human-comprehensible fuzzy systems played a key role …

Over the past few decades, fuzzy systems have been widely used in several application
fields, thanks to their ability to model complex systems. The design of fuzzy systems has …

Artificial neural networks (ANN) have been widely used and have achieved remarkable
achievements. However, neural networks with high accuracy and good performance often …

In recent times, the progress of machine learning has facilitated the development of decision
support systems that exhibit predictive accuracy, surpassing human capabilities in certain …

Fuzzy inference systems (FIS) are likely to play a significant part in system modeling,
provided that they remain interpretable following learning from data. The aim of this paper is …

Interpretability is acknowledged as the main advantage of fuzzy systems and it should be
given a main role in fuzzy modeling. Classical systems are viewed as black boxes because …

In this paper, we propose an index that helps preserve the semantic interpretability of
linguistic fuzzy models while a tuning of the membership functions (MFs) is performed. The …

This paper presents the implementation and explanations of a remaining life estimator
model based on machine learning, applied to industrial data. Concretely, the model has …

The present paper aims to demonstrate the interest of fuzzy inference systems in system
modeling when human interaction is important. It discusses the originality of FIS and their …