摘要
通过分析中医临床数据的特性,将临床数据分为低层级数据和高层级数据,每个层级数据又分为全局输入参数和局部输入参数。基于这些概念,建立了一种两层级神经网络,低层级子神经网络局部处理低层级数据,高层级子神经网络综合处理高层级数据和低层级子神经网络的输出结果。这样的结构不仅能有效地刻画中医辨证问题,而且简化了计算,提高了学习收敛速度。实验结果表明,这种两级神经网络可以较好地应用于具有复杂数据关系的中医辨证智能计算。
By research on its characteristics, the traditional Chinese medicine （TCM） clinical data are divided into two layergrades ： low layer-grade of data and high layer-grade of data. And every layer-grade data are divided into global data and local data. Based on this concept, built a two layer-grade neural network, which its low layer-grade sub-neural network calculated low layer-grade data and its high layer-grade sub-neural network comprehensively processed the high layer-grade data and the output of the low layer-grade sub- neural network. In this structure, not only was the TCM categorical identification problem effectively described, but also the calculation was simplified and the convergent learning speeded up. The result of experiments indicates that the two-layer neural network can be well applied to the TCM categorical identification intelligent calculation with complex data relationships.
出处
《计算机应用研究》
CSCD
北大核心
2008年第10期3169-3170,3173,共3页
Application Research of Computers
基金
国家自然科学基金资助项目(30472122)
国家中医药管理局重点资助项目(2000-J-Z-01)
关键词
层级神经网络
中医辨证
智能诊断
layers-grade of neural network
traditional Chinese medicine （TCM） diagnosis
intelligent diagnosis
