Scope & Topics

International Journal of Fuzzy Logic Systems (IJFLS) is an open access peer-reviewed journal that covers all topics in theoretical, experimental and applied fuzzy techniques and systems. It is aimed to bring together researchers and developers from both academia and industry to discuss the latest scientific and theoretical advances in this field, and to demonstrate the state-of-the-art systems. The journal solicits original technical papers that were not previously published and are not currently under review for publication elsewhere.

Foundations of Fuzzy Logic and Uncertainty Modeling


• Fuzzy logic theory, algorithms and computation
• Generalized fuzzy sets, type 2 and higher order fuzzy systems
• Fuzzy measures, integrals and uncertainty quantification
• Possibility theory, imprecise probabilities and belief structures
• Approximate reasoning and soft computing frameworks

Fuzzy Systems in Artificial Intelligence and Machine Learning


• Neuro fuzzy learning systems and deep neuro fuzzy architectures
• Fuzzy logic in deep learning, reinforcement learning and large scale neural networks
• Fuzzy clustering, fuzzy pattern recognition and fuzzy feature extraction
• Fuzzy logic for explainable AI (XAI) and interpretable machine learning
• Fuzzy logic in trustworthy, safe and robust AI
• Fuzzy logic for big data analytics and high dimensional learning
• Fuzzy logic for data efficient, online and incremental learning

Hybrid and Computational Intelligence Systems


• Hybrid fuzzy systems (fuzzy neuro evolutionary probabilistic rough)
• Fuzzy logic integrated with swarm intelligence and evolutionary computation
• Fuzzy Bayesian models and probabilistic fuzzy hybrid reasoning
• Fuzzy logic in multi agent systems and distributed intelligence

Modelling, Control and Optimization


• Fuzzy modeling, system identification and adaptive fuzzy systems
• Intelligent control, fuzzy control of nonlinear and uncertain systems
• Fuzzy optimization and multi objective decision making
• Fuzzy decision analysis, decision support systems and risk assessment
• Fuzzy logic in autonomous systems, robotics and intelligent vehicles