Guest Editorial: Deep Fuzzy Models

Alexander Gegov, Uzay Kaymak, Joao Miguel da Costa Sousa

Research output: Contribution to journalEditorialAcademicpeer-review


The papers in this special section focus on recent developments and emerging topics in the area of deep fuzzy models that address some of the problems and limitations above. These models have been known under different names, such as hierarchical fuzzy systems and fuzzy networks. They are usually well suited for performing multiple functional compositions at either crisp or linguistic level. Deep learning has gained significant attention within the computational intelligence community in recent years. Its success has been mainly due to the increased power of modern computational platforms in terms of their ability to collect, store, and process large volumes of data. This has led to a substantial increase in the effectiveness and efficiency of data management. As a result, it has become possible to achieve high accuracy for some benchmark learning tasks, such as object classification and image recognition within a short time frame. The most common implementation of deep learning has been through neural networks due to the ability of their layers of neurons to perform multiple functional compositions as part of a multistage learning process.
Original languageEnglish
Article number9130630
Pages (from-to)1191-1194
Number of pages4
JournalIEEE Transactions on Fuzzy Systems
Issue number7
Publication statusPublished - Jul 2020


  • Special issues and sections
  • Deep learning
  • Fuzzy systems
  • Benchmark testing
  • Feature extraction
  • Computational modeling


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