Applied Fuzzy Logic and Soft Computing to Real World Problems

Dear Colleagues,

Soft computing is a set of artificial intelligence techniques that focus on modeling and solving complex problems that are difficult to address with traditional mathematical methods based on rigid and precise rules. Soft computing is capable of handling imprecise, uncertain, or incomplete data and consists of three main approaches: fuzzy logic, neural networks, and evolutionary or bio-inspired computing.

Soft computing applications are numerous and are used in diverse fields such as medicine, engineering, economics, robotics, and data science. In medicine, for example, it is used to analyze medical images and to help doctors diagnose diseases such as cancer. In engineering, it is used to design automatic control systems that can adapt to variable conditions and can change their behavior according to the situation. In data mining, it is used to analyze large amounts of information and to extract patterns and trends that can be useful for making decisions.

In robotics, it is used to create robots that can interact with their environment in a more natural and adaptive way. Robots that use artificial intelligence techniques are able to learn and adapt to their environment, allowing them to perform complex tasks in dynamic and changing environments.

In conclusion, the use of intelligent computing techniques such as bio-inspired optimization algorithms, fuzzy logic control, and neural networks applied to problem-solving has achieved significant technological advancement. 

This Special Issue invites all researchers to report and share the results obtained from their research work. 

Potential themes include but are not limited to the following: 

• Novel nature-inspired or application-inspired optimization algorithms;
• Statistical approaches for understanding the behavior of nature-inspired methods;
• Parameter adaptation using mathematical fuzzy models;
• Intelligent agents;
• Mathematical fuzzy logic and intelligent and automatic control;
• Optimization of neurocomputing systems;
• Methods based on collective intelligence;
• Artificial intelligence-based models;
• Artificial neural networks;
• Federated learning models. 

I/We look forward to receiving your contributions.

Dr. Camilo Caraveo
Dr. Leticia Cervantes
Guest Editors

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• intelligent computing
• fuzzy logic control
• intelligent control
• optimization problems
• intelligent agents
• neural networks