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Special Issue "Type-2 Fuzzy Logic: Theory, Algorithms and Applications"
A special issue of Axioms (ISSN 2075-1680).
Deadline for manuscript submissions: 20 December 2019.
Interests: fuzzy logic; type-2 fuzzy logic; fuzzy control; hybrid intelligent systems
Special Issues and Collections in MDPI journals
In 1965, Prof. L. Zadeh introduced the concept of fuzzy sets (FSs) to represent uncertain system parameters. However, in many real-world systems, uncertainty appears for multiple reasons. In such a scenario, uncertainty modelling capabilities of type 1 (T1) or traditional FSs are quite limited, so Zadeh himself came up with the concept of type-2 FSs in 1975. However, for more than a decade, these types of FSs got very little attention from the scientific community. Interestingly, from 1990, researchers started investigating the T2 FSs, or more specifically the interval type-2 (IT2) FSs, and successfully applied the same concept for realistic uncertainty modelling in a number of applications.
Very recently, a new research trend has been noticed, in which researchers have shifted their focus from the IT2 FSs to the general type 2 (GT2) FSs and explored better results in many applications. This has further been motivated by some of Prof. J. M. Mendel´s recent works, in which he has nicely shown that if proper care is taken during the designing phase, an IT2 fuzzy logic system (FLS) shall always produce better (or at least equal) performance than a T1 FLS. Similarly, a GT2 FLS has the capability to give better than (or at least equal performance to) a IT2 FLS. Nevertheless, the growth of research carried out on the T2 FSs and T2 FLSs is far less than the volume of research conducted on T1 FSs. Therefore, this Special Issue aims to introduce cutting-edge research concepts on T2 FSs and systems and their applications in a number of emerging systems including (but not limited to) the following:
- T2 FS-based uncertainty modelling in Cyber-physical systems
- Social network analysis under T2 fuzzy uncertainty
- T2 FLSs in cyber security
- T2 FS-based uncertainty modelling in big data analytics
- Multi-media applications with fuzzy uncertainty
- T2 FSs for image processing
- T2 FSs in evolutionary optimization
- T2 FSs and T2 FLSs in machine learning
- T2 FSs and T2 FLSs deep learning
- T2 FLSs for power systems
- T2 FSs for energy optimization
- T2 FSs and T2 FLSs green computing
- T2 FS-based uncertainty modelling vehicle routing problem
- And other application areas with T2 FS-based uncertainty modelling
Prof. Dr. Oscar Castillo
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Axioms is an international peer-reviewed open access quarterly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- type-2 fuzzy logic
- type-2 fuzzy control
- type-2 fuzzy pattern recognition
- type-2 fuzzy neural networks
- type-2 fuzzy in metaheuristics