Ying-Jeng Wu

National Yunlin University of Science and Technology

Papers

Total Citations

H-Index

About

Ying-Jeng Wu is a researcher whose work bridges intelligent control systems and optimization methodologies, with a particular focus on grey-fuzzy logic and genetic algorithms. His most-cited paper, "Optimal Grey-Fuzzy Gain-Scheduler Design Using Taguchi-HGA Method" (2001), has garnered 7 citations, reflecting its niche but impactful contribution to the field of adaptive control. In this work, Wu introduced a novel hybrid approach that integrates grey theory with fuzzy logic and a Taguchi-based hybrid genetic algorithm (HGA) to design robust gain-scheduling controllers. This innovation addresses key challenges in system uncertainty and parameter tuning, offering a systematic method for optimizing controller performance under varying conditions. Wu’s contributions are particularly notable for their practical applicability in engineering systems requiring adaptive and resilient control, such as robotics and industrial automation. While his citation count is modest, the specificity and technical depth of his work have influenced subsequent studies in grey-fuzzy optimization and metaheuristic design. For students and researchers exploring intelligent control or evolutionary computation, Wu’s paper serves as a foundational reference for combining grey systems theory with advanced optimization techniques, demonstrating how hybrid frameworks can yield efficient and reliable solutions to complex control problems.