This study provides a comprehensive review of Türksen's Fuzzy Functions (FF) approach, examining its methodological evolution and research landscape over nearly 2 decades to identify theoretical advances, application patterns and future research directions.
A comprehensive systematic literature review with bibliometric and social network analysis is conducted. The methodological review synthesizes 76 publications. The developments are categorized into seven evolutionary pathways: early developments, clustering enhancements, evolutionary computation integration, higher-order uncertainty modeling, recurrent architectures, robustness considerations and ensemble learning. Bibliometric analysis examines productivity patterns, citation structures, author collaboration networks, institutional distributions and thematic organization.
The FF approach has evolved from basic FF-LSE models to sophisticated architectures incorporating advanced clustering algorithms, evolutionary optimization, type-2/intuitionistic/picture fuzzy sets, recurrent structures and robust estimation. Bibliometric analysis reveals cyclical productivity with peaks in 2020 and 2022, strong geographic concentration in Turkish institutions, fragmented collaboration networks with minimal cross-institutional bridges and application focus on financial forecasting. Critical gaps include incomplete theoretical formalization of higher-order variants, limited international diffusion and insufficient attention to model interpretability.
This study presents the most comprehensive bibliometric analysis of FF research, systematic categorization of methodological innovations, quantitative mapping of application domains and integrated identification of theoretical gaps and methodological challenges, establishing a structured research agenda for future efforts.
