Please use this identifier to cite or link to this item:
Title: GA-based learning of BMF fuzzy-neural network
Authors: 國立臺灣師範大學電機工程學系
W.-Y. Wang
T.-T. Lee
C.-C. Hsu
Y.-H. Li
Issue Date: 17-May-2002
Abstract: An approach to adjust both control points of B-spline membership functions (BMFs) and weightings of fuzzy-neural networks using a simplified genetic algorithm (SGA) is proposed. The SGA is proposed by using a sequential-search-based crossover point (SSCP) method in which a better crossover point is determined and only the gene at the specified crossover point is crossed as a single point crossover operation. 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 SGA. Because of the use of the SGA, faster convergence of the evolution process to search for an optimal fuzzy-neural network can be achieved. Nonlinear functions approximated by using the fuzzy-neural networks via the SGA are demonstrated to illustrate the applicability of the proposed method
Other Identifiers: ntnulib_tp_E0604_02_078
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.