Adaptive bound reduced-form genetic algorithms for B-spline neural network training

Wei Yen Wang*, Chin Wang Tao, Chen Guan Chang

*此作品的通信作者

研究成果: 雜誌貢獻期刊論文同行評審

11 引文 斯高帕斯(Scopus)

摘要

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.

原文英語
頁(從 - 到)2479-2488
頁數10
期刊IEICE Transactions on Information and Systems
E87-D
發行號11
出版狀態已發佈 - 2004 十一月

ASJC Scopus subject areas

  • 軟體
  • 硬體和架構
  • 電腦視覺和模式識別
  • 電氣與電子工程
  • 人工智慧

指紋

深入研究「Adaptive bound reduced-form genetic algorithms for B-spline neural network training」主題。共同形成了獨特的指紋。

引用此