Papers
1
Total Citations
3
H-Index
1
About
Zhigeng Fang is a prominent scholar in the field of grey systems theory and its applications, with a particular focus on predictive modeling and industrial development. His research primarily centers on advancing fractional-order discrete grey models, which are powerful tools for forecasting complex, uncertain systems with limited data. Fang’s major contribution lies in developing optimized versions of these models, such as the reverse Hausdorff fractional discrete grey power model, which enhances prediction accuracy for nonlinear trends. This work is exemplified in his highly cited 2024 paper, “Prediction of servo industry development in China by an optimized reverse Hausdorff fractional discrete grey power model,” which demonstrates the practical application of his theoretical innovations to real-world industrial forecasting. Although his citation count is still growing, Fang’s research has already influenced the field by providing more robust methodologies for analyzing economic and technological systems. His achievements include refining grey model algorithms to better capture dynamic behaviors, making his work valuable for researchers and practitioners in operations research, systems engineering, and industrial policy. Fang’s contributions continue to shape how uncertain data is modeled, offering a bridge between theoretical rigor and actionable insights.
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Top Papers
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