神经网络与多元统计在复杂化学信息模式分类中的集成应用

天然有机物料的化学组成十分复杂，其组分通常高达几百种，从而使其组成与性质间自关系，即构效关系，难以分辨、识别和确定．例如白酒、香料的构效关系就难以用化学机理描述．通常采用数据处理方法，将其归结为模式分类问题．复杂化学信息模式分类问题有模三维数高、样本容量小等特点，采用人工神经网络（ANN）具有一定的优势和不足．文献［1，2］讨论了从网络训练算法出发，改善ANN运行性能的技术．本文拟从另一角度探讨该问题．1对输入模式的分析当物料的组分数增多时，构效关系的复杂程度将剧增．为提高神经网络的表达和处理有力，必…

Integrating Multivariate Statistical Analysis with Neural Networks for Pattern Classification of Complex Chemical Information

A new method by integrating the multivariate statistical analysis with neural network used for complex pattern classification was proposed in this paper. First, a particularly developed statistical method called correlational components analysis was employed to extract pattern characteristics from the original sample pattern space. These pattern characteristics were then used as inputs to a multi-layered feedforward neural networks for further pattern classification, The proposed approach transforms the complex patterns into lower dimensional and mutually decoupled ones, it also takes the advantages of the self-learning capability of the neural networks. Finally, a practical example of natural spearmint oil was used to verify the effectiveness of the new method. The results showed that the proposed integrated approach gives better results than other conventional methods.