Professor, Central South University, China
👨🏫 Hui Peng is a distinguished Professor in the School of Automation at Central South University, Changsha, China. With a prolific career spanning over two decades, he has authored more than 70 papers in international journals and co-invented a patent in Japan. His expertise lies in various aspects of control engineering and statistical science, making significant contributions to industrial process control projects globally.
Profile
ORCID
Education
🎓 Hui Peng earned his B.Eng. and M.Eng. degrees in Control Engineering from Central South University, Changsha, China, in 1983 and 1986, respectively. He obtained his Ph.D. in Statistical Science from the Graduate University for Advanced Studies, Hayama, Japan, in 2003.
Experience
💼 Hui Peng has been a Professor at the School of Automation, Central South University, since 1998. He served as a Visiting Professor at the Institute of Statistical Mathematics, Tokyo, Japan, from 2000 to 2004 and again from 2009 to 2010. Since 2010, he has also been a Foreign Cooperative Professor at the Graduate University for Advanced Studies, Japan. His work includes successfully completing over ten industrial process control projects involving diverse systems like marine ships, thermal power plants, and quadrotor helicopters.
Research Interests
🔬 Hui Peng’s research interests are extensive and include nonlinear system modeling, statistical modeling, system identification, nonlinear optimization, signal processing, predictive control, robust control, process control, financial process modeling, and portfolio optimization.
Awards
🏆 Hui Peng has not only authored numerous papers but also co-invented a patent in Japan, reflecting his significant contributions to control engineering and statistical science.
Publications
Market price, excess demand and liquidity dynamics modeling and application to investment decision optimization
Research on Time Varying Variance Financial Time Series Modeling and Portfolio Optimization Methods
State-dependent ARX model-based nonlinear system modeling and robust predictive control