報告題目：Unleashing the Power of Fuzzy Logic: Intelligent Control and Machine Learning Integration for Real-World Applications
報告人：Hak-Keung Lam
報告時間：2023年7月27日上午9：00
報告地點：圖書館綜合樓T1612
主辦單位：科技處,、研究生部,、數理與金融學院
報告人簡介：Hak-Keung Lam (Fellow, IEEE) is a distinguished researcher with a strong academic background. He obtained his B.Eng. (Hons.) and Ph.D. degrees in electronic and information engineering from the Hong Kong Polytechnic University. His research interests encompass intelligent control, computational intelligence, and machine learning. He is actively engaged in the academic community, serving as a Program Committee Member, International Advisory Board Member, Invited Session Chair, and Publication Chair for numerous international conferences. He is also a Reviewer for various books, international journals, and conferences. His expertise has led him to become an Associate Editor for prestigious journals such as IEEE Transactions on Fuzzy Systems, IEEE Transactions on Circuits and Systems II: Express Briefs, and more. He is also a Guest Editor and sits on the editorial board of several international journals. Notably, he has been recognized as a highly cited researcher. Furthermore, he has made significant contributions to the field through his authorship and co-authorship of three monographs and co-editorship of two edited volumes. His publications include Stability Analysis of Fuzzy-Model-Based Control Systems, Polynomial Fuzzy Model-Based Control Systems, and Analysis and Synthesis for Interval Type-2 Fuzzy-Model-Based Systems.
報告摘要：This presentation aims to provide an introductory overview of the integration of intelligent control and machine learning, two powerful disciplines in the field of automation and decision-making. It will cover fundamental concepts such as open/closed-loop control systems and models, highlighting their roles in regulating and optimizing dynamic processes. The significance of fuzzy logic will also be discussed, showcasing its ability to handle uncertainties and complex systems that are difficult to model using traditional control techniques. By employing fuzzy logic, we can create adaptive and robust control systems capable of handling real-world variations and uncertainties. Throughout the talk, various applications will be explored, ranging from robot control in manufacturing environments to drug administration in healthcare settings. Additionally, we will delve into biomedical engineering problems where intelligent control and machine learning techniques are used to enhance diagnostics and treatment. Furthermore, the presentation will demonstrate how to synergize intelligent control techniques with machine learning algorithms for both lab-based research and practical applications. By integrating these approaches, we can achieve enhanced performance and efficiency in control systems, leading to optimized outcomes in diverse real-life scenarios. This talk will offer valuable insights into the seamless fusion of intelligent control and machine learning, providing attendees with a broader perspective on cutting-edge research and its practical applications across various domains.