摘要
提出了对象及其特征数据的一些特性指标:对象的相似度、复杂度、隐蔽度(或能见度),特征数据的贡献度、常见度、显隐性。在综合分析这些特性的基础上,通过融合模糊神经网络技术及可拓学思想,研究了一种信息非完全的复杂数据智能化处理拓展算法,通过嵌入竞争神经网络的计算模型实现了该算法。在复杂的中医诊断推理过程的应用结果表明,该算法可以较好地应用于处理复杂的中医临床数据。
This paper presented some characteristic parameters of the object and its character data ; the object's similarity degree, complexity degree, and invisibility degree( or visibility degree) ; and the character data' s contribution degree , rate of appearance, external ( or presentational ) and invisible character. On the basis of the characteristics was synthetically analyzed, through combining fuzzy neural network and extenlcs thinking, this article researched an intelligent processing expan- dable algorithm on incomplete and complex data, and by embedding competition neural network' s computing model, realized the algorithm. The application result in the complex Chinese medicine diagnosis process make clear that, the algorithm can be applied to process perplexing Chinese medicine clinical data.
出处
《计算机应用研究》
CSCD
北大核心
2008年第5期1328-1329,1332,共3页
Application Research of Computers
基金
国家自然科学基金资助项目(30472122)
关键词
智能处理
拓展算法
模糊神经网络
中医诊断
intelligent reasoning
expandable algorithm
fuzzy neural network
Chinese medicine diagnosis