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This paper aims to investigate the state-of-the-art of existing deep fuzzy systems (DFS) for regression, <jats:italic>i.e.<\/jats:italic>, methods that combine DL and FLS with the aim of achieving good accuracy and good interpretability. Within the concept of explainable artificial intelligence (XAI), it is essential to contemplate interpretability in the development of intelligent models and not only seek to promote explanations after learning (post hoc methods), which is currently well established in the literature. Therefore, this work presents DFS for regression applications as the leading point of discussion of this topic that is not sufficiently explored in the literature and thus deserves a comprehensive survey.<\/jats:p>","DOI":"10.1007\/s40815-023-01544-8","type":"journal-article","created":{"date-parts":[[2023,6,5]],"date-time":"2023-06-05T07:02:16Z","timestamp":1685948536000},"page":"2568-2589","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["Survey on Deep Fuzzy Systems in Regression Applications: A View on Interpretability"],"prefix":"10.1007","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6263-3602","authenticated-orcid":false,"given":"Jorge","family":"S. S. 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