文献类型：期刊文献

中文题名：复杂化学模式群的非线性映射及其应用

英文题名：The Non-linear Map of the Group of Complex Chemical Patterns and Its Application

作者：颜学峰[1];陈德钊[2];胡上序[2]

机构：[1]华东理工大学自动化研究所,上海200237;[2]浙江大学化工系计算机仿真教研室,杭州310027

年份：2003

卷号：31

期号：8

起止页码：928

中文期刊名：分析化学

外文期刊名：Chinese Journal of Analytical Chemistry

收录：CSTPCD;;Scopus;北大核心:【北大核心2000】；CSCD:【CSCD2011_2012】；

语种：中文

中文关键词：复杂化学模式群;非线性映射;拓扑结构;类随机优化算法;聚类;橄榄油

外文关键词：complex chemical pattern; non-linear map; topology-preserving; pseudo-similar random optimization approach; cluster

摘要：提出新的非线性映射算法 ,并分别采用传统非线性映射算法和新的非线性映射算法 ,将 8维橄榄油样本映射于平面。其中新的非线性映射算法获得更好保留样本模式拓扑结构的映射平面 ,映射平面清晰地反映模式的类别关系 ,即同类模式都清晰地聚集在一起 ,实现聚类。
Non-linear mapping (NLM) was applied to project high-dimensional complex chemical information down on the two-dimensional plane, while the topology-preserving map was obtained, on which the important relationship among data can be visualized easily. Furthermore, a modified NLM (M-NLM) with a new algorithm of adaptive mapping error and a novel pseudo-similar random optimization approach was proposed to overcome the deficiencies of conventional NLM (C-NLM). Finally, a typical example of mapping eight-imensional olive oil samples onto two-dimensional plane was employed to verify the effectiveness of M-NLM and C-NLM. The results show that the topology-preserving map obtained by M-NLM can well represent the classification of original patterns and is much better than that obtained by C-NLM.