基于对向传播神经网络的酚类化合物毒性的模式识别研究
Pattern Recognition of Toxioty of Phenolic Compound Based on Counter Propagation Network
申明金1
作者信息
- 1. 川北医学院药学院,四川南充 637000
- 折叠
摘要
介绍了对向传播神经网络的原理、算法.以酚类化合物的 8 个结构特征参数为输入,用对向传播神经网络对酚类化合物的毒性进行模式分类识别.结果表明,对向传播神经网络具有较强的模型拟合能力和泛化能力.网络对 36 个训练样本和 8 个预测样本的毒性类型都能进行准确识别,是一种有效的模式分类识别方法.
Abstract
This paper introduces the principle and algorithm of counter propagation network.Which using eight structural characteristic parameters of phenolic compounds as inputs to classify and recognize the toxicity of phenolic compound by counter propagation network.The results indicate that the counter propagation network has strong model fitting and generalization abilities.The network can accurately identify the toxicity types of 36 training samples and 8 prediction samples.It is an effective pattern classification recognition method.
关键词
对向传播神经网络/酚类化合物/毒性/模式识别Key words
counter propagation network/phenolic compound/toxicity/pattern recognition引用本文复制引用
出版年
2024
NETL
NSTL
万方数据