Please use this identifier to cite or link to this item: http://rportal.lib.ntnu.edu.tw:80/handle/77345300/32060
Title: A GA-based indirect adaptive fuzzy-neural controller for uncertain nonlinear systems
Authors: 國立臺灣師範大學電機工程學系
W.-Y. Wang
C.-C. Hsu
C.-W. Tao
Y.-H. Li
Issue Date: 6-Dec-2002
Abstract: In this paper, a novel approach to adjust both the control points of B-spline membership functions (BMFs) and the weightings of fuzzy-neural networks using a reduced-form genetic algorithm (RGA) is proposed. Chromosomes consisting of both the control points of BMFs and the weightings of fuzzy-neural networks are coded as an adjustable vector with real number components and searched by the RGA. Moreover, we propose an application of the RGA in designing an RGA-based indirect adaptive fuzzy-neural controller (RIAFC) for uncertain nonlinear dynamical systems. The free parameters of the indirect adaptive fuzzy-neural controller can successfully be tuned on-line via the RGA approach. A supervisory controller is incorporated into the RIAFC to stabilize the closed-loop nonlinear system. An example of a nonlinear system controlled by RIAFC are demonstrated to show the effectiveness of the proposed method.
URI: http://rportal.lib.ntnu.edu.tw/handle/77345300/32060
Other Identifiers: ntnulib_tp_E0604_02_083
Appears in Collections:教師著作

Files in This Item:
There are no files associated with this item.


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.