基于自组织神经网络的汉字识别模拟研究  

Simulation Research of Chinese Characters Recognition Based on Self-organizing Neural Network

作　　者：陈静[1] 穆志纯[1] 方新[1] 杜大鹏[1] 

机构地区：[1]北京科技大学信息工程学院,北京100083

出　　处：《计算机工程》2007年第11期170-172,共3页Computer Engineering

基　　金：北京市教委重点学科共建基金资助项目(XK100080537);北京语言大学校规划基金资助项目(04GH01)

摘　　要：汉字识别是汉语、汉字认知研究的一个重要研究领域。该文提出了一个基于多层自组织神经网络的模型,从汉字字形聚类及汉字部件拆分的角度,对基于汉字认知的汉字识别过程进行了初步的探索。模拟研究结果表明,模型通过学习能够识别出汉字的结构类型和部件,发现汉字识别中的规律,在一定程度上模拟了汉字的识别。Chinese characters recognition is an important research field in Chinese characters cognition. This paper proposes a model based on multi-layer self-organizing neural network. The process of Chinese characters recognition based on Chinese characters cognition is researched from the aspect of Chinese characters cluster and components splitting. Results from this simulation suggest that the model is able to recognize the architecture and components of Chinese characters. It can detect some rules of Chinese characters recognition by learning. So it can simulate the process of Chinese characters recognition to some exteht.

关 键 词：汉字认知 汉字识别 自组织特征映射 聚类 计算机模拟 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]