关键词: 中医辨证, 神经网络, 专家系统, 不确定性推理, 中医

Abstract: In this paper, a modified Back-Propagation (BP) Artificial Neural Network (ANN) is applied to realizing the uncertainty inference of syndrome differentiation in Traditional Chinese Medicine (TCM), which uses two kinds of knowledge representation methods: certainty factor method and certainty interval method. First, the two methods of uncertainty knowledge representation of syndrome differentiation in TCM are proposed. Second, BP network and its modified train function are presented. Third, how to apply the modified BP network to the uncertainty inference of syndrome differentiation in TCM is illustrated in detail. Finally, two simulative examples using the MATLAB neural toolbox, which include both certainty factor method and certainty interval method for each example, are given and analyzed. The simulation results show that ANN can play an simple, but important and effective role in the uncertainty inference of syndrome differentiation in TCM, and that ANN can not only learn automatically experts~{!/~} experiences, but also has the capability of generalizing the learned experience into more general situations according with experts~{!/~} minds.

Key words: Syndrome Differentiation, Neural Network, Expert System, Uncertainty Reasoning, Traditional Chinese Medicine (TCM)