國立臺灣師範大學電機工程學系W.-Y. WangC.-C. HsuC.-W. TaoY.-H. Li2014-10-302014-10-302002-12-06http://rportal.lib.ntnu.edu.tw/handle/20.500.12235/32060In 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.模糊類神經網路遺傳演算法適應控制非線性系統函數近似Fuzzy Neural NetworkGenetic AlgorithmAdaptive ControlNonlinear SystemFunction Approximation