請用此 Handle URI 來引用此文件: http://hdl.handle.net/11455/95510
標題: Nonlinear Systems Identification and Control Using Uncertain Rule-based Fuzzy Neural Systems with Stable Learning Mechanism
作者: 李慶鴻
Ching-Hung Lee
Yi-Han Lee
Chih-Min Lin
關鍵字: Fuzzy neural system
Uncertainty
Rule-based
Lyapunov theorem
System identification
Robot manipulator
出版社: INTERNATIONAL JOURNAL OF FUZZY SYSTEMS
摘要: This paper proposes an uncertain rule-based fuzzy neural system (UFNS-S) with stable learning mechanism for nonlinear systems identification and control. The proposed UFNS-S system not only preserves the ability of handling uncertain information but also performs less computational effort. The sinusoidal perturbations are adopted to combine with the fuzzy term sets of UFNS-S. For training the UFNS-S systems on system identification and control applications, the gradient descent method with adaptive learning rate is derived. This guarantees the convergence of UFNS-S by choosing adaptive learning rates which enhance the convergent speed. This provides a simple way for choosing the learning rates for training the UFNS-S which also guarantees convergence and faster learning. Finally, the effectiveness and performance of the proposed approach is illustrated by several examples, computational complexity analysis, nonlinear system identification, and tracking control of two-link robot manipulator system.
URI: http://hdl.handle.net/11455/95510
文章連結: https://link.springer.com/article/10.1007/s40815-016-0170-4
顯示於類別:機械工程學系所

文件中的檔案:
沒有與此文件相關的檔案。


在 DSpace 系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。