摘要
在运用神经元计算技术对高维小样本复杂化学模式进行分类时,通过模式特征提取,降低输入变量维数,能使复杂的模式分类问题比较容易解决.根据模式类别相关分步分析思路,提出复杂化学模式特征分步提取法,可将原始模式数据中与类别指标相关较大的特征量有效地提取出来.应用于天然植物组效关系辨识结果表明,这种化学模式特征提取方法比经典主成分分析法更为实用可靠.
The neural computation technology is often used for chemical pattern classification. It is rather difficult to apply neural networks for classifying complex chemical pattern, which has the property of high - dimension but low - sample - number. By extracting pattern characteristic, decreasing the dimension of network input, this problem in complex pattern classification can be relatively easily solved. Based on the principal of searching class correlative component, a new method, named stepwise class correlative components analysis (SCCCA), is proposed. The technique can extract characteristic component that has relatively large correlative value with the class measurement from the original dataset. Comparing with principal component analysis (PCA), a typical example in identifying the composition - activity relationship of a natural plant was used, and the results verified that the new method is better than PCA.
出处
《化学学报》
SCIE
CAS
CSCD
北大核心
2001年第6期842-846,共5页
Acta Chimica Sinica
基金
国家自然科学基金，国家重点基础研究发展计划(973计划)
关键词
化学模式分类
模式特征提取
分步相关分析
分类神经网络
中药
天然植物
组效关系辨识
chemical pattern recognition
pattern characteristic extraction
stepwise class correlative components analysis
classifying neural networks
