Please use this identifier to cite or link to this item: http://rportal.lib.ntnu.edu.tw:80/handle/77345300/31955
Title: Adaptive bound reduced-form genetic algorithms for B-spline neural network training
Authors: 國立臺灣師範大學電機工程學系
W.-Y. Wang
C.-W. Tao
C.-G. Chang
Issue Date: 1-Nov-2004
Publisher: IEICE
Abstract: In this paper, an adaptive bound reduced-form genetic algorithm (ABRGA) to tune the control points of B-spline neural networks is proposed. It is developed not only to search for the optimal control points but also to adaptively tune the bounds of the control points of the B-spline neural networks by enlarging the search space of the control points. To improve the searching speed of the reduced-form genetic algorithm (RGA), the ABRGA is derived, in which better bounds of control points of B-spline neural networks are determined and paralleled with the optimal control points searched. It is shown that better efficiency is obtained if the bounds of control points are adjusted properly for the RGA-based B-spline neural networks.
URI: http://rportal.lib.ntnu.edu.tw/handle/77345300/31955
ISSN: 0916-8532
Other Identifiers: ntnulib_tp_E0604_01_033
Appears in Collections:教師著作

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