基于BP神经网络的中药性味归经与补虚药药效研究
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摘要
基于BP神经网络构建76味补虚药分类模型。采用数据挖掘方法挖掘补虚药药效,利用Python语言实现BP神经网络,并构建药效分类模型及其应用。对12味补虚药进行测试,其中有10味预测结果正确,正确率为83.33%。通过药性分布可知,补虚药的主要特征为温、甘,归经为脾、肾、心经。采用中药性味归经为特征对中药分类进行预测,实验结果表明,基于BP神经网络的药效分类模型具有一定的可靠性和准确性,亦为中药分类方法研究提供了借鉴。
Based on the BP neural network,a classification model of 76 flavored drugs was established. Data mining method was used to mine the efficacy of tonic drugs,and BP neural network was used to construct the pharmacodynamic classification model and its application. 10 of the 12 flavours were tested correctly with a correct rate of 83.33%. Through the distribution of drug,we can see that the main characteristics of reinforcing deficiency medicine are warm and sweet,and the meridian is the spleen,kidney and heart meridian. In this study,the characteristics of flavor meridian of traditional Chinese medicine were used to predict the classification of traditional Chinese medicine. The experimental results show that the classification model based on BP neural network has certain reliability and accuracy,and provides a reference for the study of traditional Chinese medicine classification methods.
Based on the BP neural network,a classification model of 76 flavored drugs was established. Data mining method was used to mine the efficacy of tonic drugs,and BP neural network was used to construct the pharmacodynamic classification model and its application. 10 of the 12 flavours were tested correctly with a correct rate of 83.33%. Through the distribution of drug,we can see that the main characteristics of reinforcing deficiency medicine are warm and sweet,and the meridian is the spleen,kidney and heart meridian. In this study,the characteristics of flavor meridian of traditional Chinese medicine were used to predict the classification of traditional Chinese medicine. The experimental results show that the classification model based on BP neural network has certain reliability and accuracy,and provides a reference for the study of traditional Chinese medicine classification methods.
引文
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[5]刘星,毛丹卓,王正武,等.薏仁种类的近红外光谱技术快速鉴别[J].光谱学与光谱分析,2014,34(5):1259-1263.
[6]王丽琼,范琦,易珍奎,等.HPLC指纹图谱结合反向传播人工神经网络和判别分析鉴定不同的麻黄药材[J].西南师范大学学报:自然科学版,2012,37(5):73-77.
[7]汤彦丰,候占忠,王志宝,等.紫花地丁的红外光谱和径向基神经网络鉴别[J].湖北农业学,2014,53(1):132-134.
[8]李味味,章新友,仵倚,等.基于BP神经网络中药复方功效的预测研究[J].中医药导报,2016,22(16):38-41.
[9]王建,曾南,夏厚林,等.中药药性理论研究模式的新思路[J].中医杂志,2013,54(2):99-102.
[10]金锐,张冰.中药药性理论复杂性特征分析[J].中国中药杂志,2012,37(21):3340-3343.
[11]陈素红,吕圭源.“性、味结合归经”层面研究中药药性[J].中药药理与临床,2008(4):58-62.
[12]肖小河.中药药性研究概论[J].中草药,2008(4):481-484.
[13]王振国.中药药性理论现代研究:问题、思路与方法[J].山东中医药大学学报,2011,35(3):195-198.
[14]钟赣生.中药学[M].北京:中国中医药出版社,2016.
[15]SHEN X,WANG L,HAN D.Application of BP neural network optimized by artificial bee colony in intrusion detection[J].Computer Engineering,2016(2):190-194.
[16]DAS H,JENA A K,NAYAK J,et al.A novel PSO based back propagation learning-MLP(PSO-BP-MLP)for classification[M].India:Springer,2015:333-344.
[17]NIE P L,FEI D,WANG H J,et al.Short-term power load forecasting based on EMD-BP neural network[J].Control&Instruments in Chemical Industry,2016.
[18]ZHAO Z,XU Q,JIA M.Improved shuffled frog leaping algorithm-based BP neural network and its application in bearing early fault diagnosis[J].Neural Computing&Applications,2016,27(2):375-385.