高等学校化学学报 ›› 1997, Vol. 18 ›› Issue (11): 1783.

• 论文 • 上一篇    下一篇

人工神经网络用于酸性染料的分类

张瑞生, 阎爱侠, 刘满仓, 胡之德   

  1. 兰州大学化学系, 兰州 730000
  • 收稿日期:1996-08-01 出版日期:1997-11-24 发布日期:1997-11-24
  • 通讯作者: 胡之德
  • 作者简介:张瑞生, 男, 35岁, 博士, 讲师.

The Classification of Acidic Dyes with Artificial Neural Network

ZHANG Rui-Sheng, YAN Ai-Xia, LIU Man-Cang, HU Zhi-De   

  1. Department of Chemistly, Lanzhou University, Lanzhou 730000
  • Received:1996-08-01 Online:1997-11-24 Published:1997-11-24

摘要: 提出用ExtendedDelta-Bar-Delta(简称EDBD)网络对酸性偶氮染料进行分类,网络结构为4-6-5,并对网络结构进行了优化.一次分类结果与采用GCEDM[1]逐次分类的结果很好地吻合,采用EDBD网络分类,比采用GCEDM分类法简单、快速、准确.

关键词: 人工神经网络, 酸性染料, 分子连通性指数, GCEDM分类法

Abstract: The acidic dyes were classified by using Extented Delta-Bar-Delta (EDBD).Thebest structure of network was 4-6-5.The optimized learning times is about 5000.It isdifficult to classify these dyes because their structures are very similar.Compared with GCEDMand other methods which were applied formerly, the EDBDmethod have the advantanges ofmore stable classification standard, fewer parameters and quicker velocity.

Key words: Artificial neural networks, Acidic dyes, Molecular connection index, GCEDM

中图分类号: 

TrendMD: