Institute of Robotics and Automatic Information System Tianjin Key Laboratory of Intelligent Robotics
Seminar Series：Advanced Robotics & Artificial Intelligence
报告题目：Decomposed Fuzzy Systems
报告人：苏顺丰 教授（Professor Shun-Feng Su）
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.
苏顺丰教授于1991年从普渡大学取得博士学位，现为台湾科技大学电子工程系讲席教授。他是IEEE Fellow、IFSA Fellow、CACS Fellow及RST Fellow，在机器人、智能控制、模糊系统、神经网络等领域发表了300余篇论文，现在的研究兴趣包含计算智能、机器学习、虚拟现实、智能交通系统、智能家居、机器人、智能控制等。他是国际模糊系统协会（IFSA）的前任主席，IEEE系统、人和控制论协会的理事会成员，担任过多个国际会议的大会主席或程序委员会主席，现/曾担任IEEE Transactions on Cybernetics、IEEE Transactions on Fuzzy Systems、IEEE/CAA Journal Automatica Sinca和IEEE Access的编委，现为SCI期刊International Journal of Fuzzy Systems主编。