報告題目：Decomposed Fuzzy Systems
報 告 人：蘇順豐 教授
報告時間：2018年10月22日（星期一）上午9:30-11:00
報告地點：燕山校區逸夫樓621
主 辦：管理科學與工程學院
Abstract:
In the talk, a novel fuzzy structure termed as the decomposed fuzzy system (DFS) is proposed to act as the fuzzy approximator. The proposed structure is to decompose each fuzzy variable into layers of fuzzy systems and each layer is to characterize one traditional fuzzy set. Similar to forming fuzzy rules in traditional fuzzy systems, layers from different variables will form the so-called component fuzzy systems. The structure of DFS is proposed to facilitate minimum distribution learning effects among component fuzzy systems so that the learning can be very efficient. It can be seen from our experiments that even when the rule number increases, the learning time in terms of cycles is still almost constant. It can also be found that the function approximation capability and learning efficiency of the DFS are much better than that of the traditional fuzzy systems when employed in adaptive fuzzy control systems. Besides, in order to further reduce the computational burden, a simplified DFS is proposed in this study to satisfy possible real time constraints required in many applications. From our simulation results, it can be seen that the simplified DFS can perform fairly with a more concise decomposition structure. Furthermore, when used in modeling, the proposed DFS not only can have much faster convergent speed, but also can achieve a smaller testing error than those of other fuzzy systems.
報告人簡介：
蘇順豐教授，1991年獲美國普渡大學博士學位，現為臺灣科技大學電子工程系講席教授，IEEE Fellow及CACS Fellow，國際模糊系統協會（IFSA）前任主席，IEEE系統、人和控制論協會（SMC）的理事會成員和青年分會主席。蘇教授擔任過多個國際會議的大會總主席或程序委員會主席，在機器人、智能控制、模糊系統、神經網絡等領域發表論文300余篇。蘇教授目前的研究領域包括計算智能、機器學習、虛擬現實仿真、智能交通系統、智能家居、機器人、智能控制等。蘇教授現為著名國際期刊IEEE Transactions on Fuzzy Systems 、IEEE Transactions on Cybernetics、IEEE Access的副主編，Journal of the Chinese Institute of Engineers期刊的領域編委，SCI二區期刊International Journal of Fuzzy Systems的主編。
