优化强化学习路径特征分类的脉象识别法

Pulse condition recognition method based on optimized reinforcement learning path feature classification

脉象识别是中医诊断的重要手段之一。长期以来,依据个人经验进行的脉诊制约了中医的推广与发展。因此,利用传感设备进行脉象识别的研究正在逐步展开。针对神经网络识别脉象的相关研究中,存在需要大量训练数据集,以及存在处理“黑箱”和时间花销较大等问题,在强化学习的框架下,提出了一种采用马尔可夫决策和蒙特卡罗搜索的脉象图分析法。首先依据中医理论对特定的脉象进行路径分类,然后在此基础上为不同的路径选择代表性特征,最终通过对代表性特征的阈值对比完成对脉象的识别。实验结果表明,所提方法可缩减训练时间和所需资源,并可保留完整的经验轨迹;且在提高脉象识别的准确率的同时,还可解决数据处理过程中的“黑箱”问题。

Pulse condition recognition is one of the important ways of traditional Chinese medical diagnosis.For a long time,recognizing pulse condition based on personal experience restricts the promotion and development of traditional Chinese medicine.Therefore,the researches on using sensing devices for recognizing pulse condition are more and more.In order to solve the problems such as large training datasets,“black box”processing and high time cost in the research of recognizing pulse condition by neural network,a new pulse condition diagram analysis method using Markov decision and Monte Carlo search on the framework of reinforcement learning was proposed.Firstly,based on the theory of traditional Chinese medicine,the paths of specific pulse conditions were classified,and then the representative features for different paths were selected on this basis.Finally,the pulse condition recognition was realized by comparing the threshold values of the representative features.Experimental results show that,the proposed method can reduce the training time and the required resources,retain the complete experience track,and can solve the“black box”problem during the data processing with the accuracy of pulse condition recognition improved.