Guest Editor(s)
Prof. Dr. Basil K. Papadopoulos
Email: papadob@civil.duth.gr
Affiliation: Department of Civil Engineering, Democritus University of Thrace, Kimeria, Xanthi, Greece
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Research Interests: fuzzy implications and fuzzy connectives, T-norms, T-conorms, and copulas, applications of fuzzy logic in engineering, decision-making, and expert systems, fuzzy topological spaces, mathematical modelling of uncertainty in applied sciences
Dr. Stefanos Makariadis
Email: makariadis@yahoo.gr
Affiliation: Department of Civil Engineering, School of Engineering, Democritus University of Thrace, Xanthi, Greece
Homepage:
Research Interests: fuzzy implications, fuzzy connectives, and parametric fuzzy negations, applications of fuzzy logic in artificial intelligence and in the modelling of environmental and climatic variables, generation of fuzzy connectives via polynomial and algebraic methods
Summary
Over the past four decades Fuzzy Logic has matured from a purely theoretical framework into a cornerstone of modern intelligent systems. Several of its central theoretical constructs — in particular fuzzy implications, aggregation operators, and copulas — remain, however, under-exploited in real engineering and decision-making pipelines, with most practical deployments still relying on a narrow subset of classical operators.
This Special Issue aims to bridge this theory-to-practice gap by gathering rigorous, application-driven research that translates well-established but under-applied fuzzy-theoretic tools into deployable methodologies. We welcome contributions that design, parametrize, or validate fuzzy implications, T-norms, T-conorms, aggregation operators, and copulas in the context of engineering modelling, decision support, expert systems, and AI-assisted reasoning. The issue seeks studies that combine mathematical rigour with concrete case studies, reproducible experiments, and transparent reporting of modelling assumptions, computational cost, and robustness — providing the community with a reference volume at the interface of fuzzy theory and intelligent practice.
Suggested themes:
1. Design, parametrization, and benchmarking of fuzzy implications and fuzzy connectives for data-driven reasoning.
2. Applications of T-norms, T-conorms, copulas, and aggregation operators in multi-criteria decision-making and classification.
3. Fuzzy implication–based models for forecasting, state estimation, anomaly detection, and signal classification in engineering time series.
4. Hybrid fuzzy–machine-learning and fuzzy–neural-network systems for explainable and interpretable AI.
5. Fuzzy expert and decision-support systems for industrial engineering, production planning, supply chain optimization, and predictive maintenance.
Keywords
fuzzy implications, fuzzy connectives, T-norms and copulas, aggregation operators, fuzzy decision-making, fuzzy expert systems, data-driven fuzzy modelling, explainable artificial intelligence
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