Theory and Applications of Fuzzy Logic and Systems
A special issue of Axioms (ISSN 2075-1680). This special issue belongs to the section "Logic".
Deadline for manuscript submissions: 30 June 2026 | Viewed by 311
Special Issue Editor
Interests: optimization algorithms; swarm intelligence; bio-inspired algorithms; fuzzy logic; neural networks
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Mathematical methods and applications using fuzzy logic systems are a recent topic of research, and they are being used to solve complex optimization problems in many areas.
Nowadays, several approaches have been developed in this interesting area, such as Type-1 and Type-2 fuzzy models to manage the uncertainty in the best way in many problems. Also, fuzzy systems have been combined with other techniques, for example, with optimization methods, such as bee colony, dolphin algorithm, wolf search, flower pollination algorithm, genetic algorithm, firefly, mayfly, ant colony, cuckoo search, termite colony, and cat swarm.
However, how to design efficient methods and how to use these algorithms for real-world application problems. In particular, the path planning of Mobile Robot by Neural Networks, the design of Neuro-Fuzzy Models, like fuzzy neural networks, fuzzy parameter adaptation in control systems, and intuitionistic fuzzy neural networks, has some current interest. In addition, new emerging neural models have been recently proposed. In all these models, a common problem is how to obtain an optimal topology, which can be handled by bio-inspired optimization algorithms.
This Special Issue aims to bring researchers to report their latest research work on the development of fuzzy models, or new applications of existing methods in the design of topologies of neural models, parameter adaptation in control systems, and path planning of robots, etc., with the ultimate goal of exploring future research directions.
Potential themes include, but are not limited to, the following:
- Theoretical mathematical methods combining fuzzy logic;
- Novel optimization algorithms;
- Statistical approaches for understanding the behavior of nature-inspired methods;
- Optimization of Neuro-Fuzzy Models;
- Mathematical fuzzy logic models;
- Optimization of emergent neural models with nature-inspired algorithms;
- Methods based on collective intelligence;
- Fuzzy control models;
- PID models for UAVs;
- Parameter adaptation using mathematical fuzzy models.
Dr. Fevrier Valdez
Guest Editor
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 submissions that pass pre-check are 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 250 words) can be sent to the Editorial Office for assessment.
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 monthly 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 2400 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.
Keywords
- fuzzy logic
- type-1 fuzzy logic
- proportional–integral–derivative (PID)
- optimization
- parameter adaptation
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