Azlee Zabidi
Papers
2
Total Citations
76
H-Index
2
About
Dr. Azlee Zabidi is a leading researcher in nonlinear system identification and computational intelligence, with a focus on developing advanced algorithms for modeling complex dynamic systems. Her primary research areas include swarm intelligence, metaheuristic optimization, and the structure selection of Nonlinear Autoregressive Moving Average with Exogenous Inputs (NARMAX) models. Dr. Zabidi’s major contributions lie in pioneering the application of binary metaheuristic algorithms—such as Binary Particle Swarm Optimization (BPSO) and Binary Artificial Bee Colony (BABC)—to automate the simultaneous structure selection and parameter estimation of NARMAX models, a task traditionally fraught with computational challenges. Her most-cited work, “Binary Particle Swarm Optimization Structure Selection of NARMAX Model of a Flexible Robot Arm” (2016, 57 citations), demonstrates the efficacy of BPSO in identifying parsimonious models for flexible robotic systems, significantly advancing the field of nonlinear system identification. Her 2017 study on BABC (19 citations) further validated the potential of swarm-based feature selection, offering a comparative benchmark against BPSO. Dr. Zabidi’s innovative integration of bio-inspired optimization with nonlinear modeling has provided powerful tools for engineers and researchers tackling real-world problems in robotics and control systems, establishing her as a key figure in this interdisciplinary domain.
Research Focus
Key Achievements
Top Papers
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