Explainable Artificial Intelligence (XAI) has experienced a significant growth over the last few
years. This is due to the widespread application of machine learning, particularly deep …

Machine and deep learning have proven their utility to generate data-driven models with
high accuracy and precision. However, their non-linear, complex structures are often difficult …

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 …

As the generalization of fuzzy systems, the belief rule base (BRB) expert system is
transparent and interpretable. However, the interpretability of BRB has almost been ignored …

In recent years, a great number of publications have explored the use of genetic algorithms
as a tool for designing fuzzy systems. Genetic Fuzzy Systems explores and discusses this …

Not only do modeling and simulation help provide a better understanding of how real-world
systems function, they also enable us to predict system behavior before a system is actually …

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 …

The inductive learning of fuzzy rule-based classification systems suffers from exponential
growth of the fuzzy rule search space when the number of patterns and/or variables …

Although fuzzy systems demonstrated their ability to solve different kinds of problems in
various applications, there is an increasing interest on augmenting them with learning …

In today's industrial systems, economic markets, life and health-care sciences fuzzy systems
play an important role in many application scenarios such as system identification, fault …