  Regression problems have been more and more embraced by deep learning (DL)
techniques. The increasing number of papers recently published in this domain,
including surveys and reviews, shows that deep regression has captured the
attention of the community due to efficiency and good accuracy in systems with
high-dimensional data. However, many DL methodologies have complex structures
that are not readily transparent to human users. Accessing the interpretability
of these models is an essential factor for addressing problems in sensitive
areas such as cyber-security systems, medical, financial surveillance, and
industrial processes. Fuzzy logic systems (FLS) are inherently interpretable
models, well known in the literature, capable of using nonlinear
representations for complex systems through linguistic terms with membership
degrees mimicking human thought. Within an atmosphere of explainable artificial
intelligence, it is necessary to consider a trade-off between accuracy and
interpretability for developing intelligent models. This paper aims to
investigate the state-of-the-art on existing methodologies that combine DL and
FLS, namely deep fuzzy systems, to address regression problems, configuring a
topic that is currently not sufficiently explored in the literature and thus
deserves a comprehensive survey.
