Special Issue "Application of Fuzzy Control in Computational Intelligence"
Deadline for manuscript submissions: 5 May 2021.
Interests: fuzzy control; intelligent control; robust control; control application to engineering systems
Interests: fuzzy control; intelligent systems; computational intelligence; machine learning; deep learning; reinforcement learning
Special Issues and Collections in MDPI journals
Special Issue in Electronics: Intelligent Control and Its Application in Motor Drive Systems
Interests: nonlinear system; fuzzy control; adaptive control
Computational intelligence schemes are investigated with the development of a suitable framework for fuzzy logic, neural networks and evolutionary computing, neuro-fuzzy systems, evolutionary-fuzzy systems, and evolutionary neural systems. Fuzzy logic is widely used in machine control. The term "fuzzy" refers to the fact that the logic involved can deal with concepts that cannot be expressed as the "true" or "false" but rather as "partially true". Although alternative approaches such as genetic algorithms and neural networks can perform just as well as fuzzy logic in many cases, fuzzy logic has the advantage that the solution to the problem can be cast in terms that human operators can understand, so that their experience can be used in the design of the controller. This makes it easier to mechanize tasks that are already successfully performed by humans. As an intelligent control technology, fuzzy control provides a systematic method to incorporate the human experience and implement nonlinear algorithms, characterized by a series of linguistic statements, into the controller. In process control applications, some research has explored the potential of fuzzy control for machine drive applications. As an increasing trend, it is necessary to pay close attention to fuzzy control applications that will enable to identify the emerging trends in the domain.
This Special Issue on “Application of Fuzzy Control in Computational Intelligence” aims to curate novel advances in the development and application of fuzzy control in computational intelligence to address challenges in artificial intelligence and automation technology. Topics include, but are not limited to the following:
- Development of novel fuzzy control technology;
- Soft computing applications with fuzzy control;
- Hybrid fuzzy control combined with intelligent computing methods;
- Theory and application of fuzzy control for intelligent systems.
Prof. Dr. Wen-Jer Chang
Dr. Hak Keung Lam
Prof. Dr. Yongming Li
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. Processes 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 1500 CHF (Swiss Francs). Please note that for papers submitted after 31 December 2020 an APC of 2000 CHF applies. 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.
- fuzzy control technology
- hybrid intelligent systems
- intelligent computing
- neuro-fuzzy-genetic approaches
- artificial intelligence
- multi-objective optimization
- soft computing
- fuzzy systems design and optimization