Fuzzy Systems and Their Applications

Dear Colleagues,

The application trends of fuzzy logic systems are developing rapidly. Fuzzy logic is being used in more and more applications, and is rapidly becoming a standard tool for engineers and scientists.

Fuzzy logic systems are used in a wide variety of applications in science, industry, and other fields.

A wide array of the used fuzzy systems can be classified into four types:

• Type-1 fuzzy logic: Type-1 fuzzy logic uses fuzzy sets with membership degrees between 0 and 1.
• Interval Type-2 fuzzy logic: Interval Type-2 fuzzy logic uses fuzzy sets which have primary membership degrees that can be an interval number between 0 and 1, and the uncertainty involved in the secondary membership is always 1.
• General Type-2 fuzzy logic: General Type-2 fuzzy logic uses fuzzy sets that have primary membership degrees that can be an interval real number between 0 and 1 and where the uncertainty involved in the secondary membership can be a number between 0 and 1.
• Interval Type-3 fuzzy logic: Interval Type-3 fuzzy logic uses fuzzy sets that have membership degrees that can be any interval number between 0 and 1 and where the uncertainty involved in the secondary membership can be an interval number between 0 and 1.

This Special Edition analyzes the mathematical theory behind fuzzy logic systems and its applications in sciences and engineering. Potential topics include, but are not limited to, the following areas of discussion:

1. Control Systems: Fuzzy logic controllers are used in various applications, such as robotics, HVAC systems, power electronics, and industrial processes, to control nonlinear and complex systems.
2. Pattern Recognition: Fuzzy logic is used in various pattern recognition applications such as image processing, speech recognition, and handwriting recognition.
3. Decision Making: Fuzzy logic systems are used in decision-making applications such as medical diagnosis, financial decision making, and the analysis of expert systems.
4. Optimization: Fuzzy logic systems are used in optimization applications such as system identification, parameter estimation, and the optimization of complex systems.
5. Robotics: Fuzzy logic is used in robotics applications such as navigation, path planning, and motion control.
6. Forecasting: Fuzzy logic is used in forecasting applications such as weather forecasting, stock market prediction, and traffic prediction. Type-1 and Interval Type-2 fuzzy logic systems are commonly used in forecasting applications.
7. Information retrieval system (IRS): the IRS is focused on obtaining relevant information from different unstructured and structured data sources efficiently.
8. Uncommon applications of artificial intelligence. Artificial intelligence is spread over several fields of sciences, e.g., the social sciences. What are these fields of science that uncommon apply the artificial intelligence? Medical disaster management, agriculture, judiciary, politics, arts & creativity.

The future of fuzzy logic systems is bright. Fuzzy logic constitutes a powerful tool that can be used to solve a wide variety of problems. As the technology continues to develop, fuzzy logic will become even more powerful and versatile.

The primary reasons for the growth of fuzzy logic are:

• Fuzzy logic is a powerful tool for dealing with uncertainty and imprecision.
• Fuzzy logic is easy to understand and use.
• Fuzzy logic can be implemented in hardware and software.

The aim of this Special Issue is to establish and to demonstrate the modern tendencies of fuzzy logic systems (FLSs) and their applications, taking into account: a) the type of FLSs, such as type-1, interval type-2, general type-2 and interval type-3; b) using singleton or non-singleton inputs; c) the field on which the application is used; d) the methods, algorithms or procedures inside the system for obtaining the knowledge; and e) efforts to design, construct, train and optimize the models. Specifically, we aim to widen our knowledge without excluding other forms of configuration and other topics such as classification, fault detection, and diagnosis. We must include other forms that are uncommon in classical approaches such as control systems, pattern recognition, decision making, optimization, robotics, forecasting, and information retrieval systems (IRS). Our work also includes uncommon approaches such as: NP-complete problem approximation, medical issues, disaster management, agriculture, prospection, judiciary, politics, arts & creativity, leadership, and social sciences.       

This Special Issue will show the tendencies in and prominence of the implementation of the FLSs in the actual problems and challenges of the world community and establish the most usual types and kinds of FLSs used today. These possess applications in modern technologies such as ChatGPT, industry 4.0 and human–machine interaction interfaces  such as Alexa. Discussions include other topics and applications in areas of knowledge and science.  

Prof. Dr. Gerardo Maximiliano Mendez
Dr. Ismael Lopez-Juarez
Dr. María Aracelia Alcorta García
Dr. Pascual Noradino Montes Dorantes
Guest Editors

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• fuzzy logic systems
• type-1
• interval type-2
• general type-2
• interval type-3
• new technologies and applications of fuzzy logic systems