摘要
目的分析脉象识别误差大小的影响因素,提高对海量脉诊数据的处理速度,探索减小脉象主观识别误差的方法。方法运用基于Hadoop环境的Map Reduce分布式计算方法改进BP算法,采用改进的BP算法对脉诊样本数据进行自学习,从而减小拟和误差。将中医电子脉诊仪采集的脉诊数据作为神经网络输入层,采用动量-学习率自适应调整快速BP算法对神经网络进行训练。结果在训练集(75%)768 M共35 890条数据中,单机模式正确预测29 150条,正确率为81.22%;Map Redece并行改进的BP算法模式正确预测35 841条,正确率为99.86%。结论与传统BP算法相比,基于Hadoop环境的Map Reduce分布式计算方法改进的BP算法模型拟合度误差更小,精确度更高。
Objective To analyze the factors of errors in the pulse recognition; To improve the speed of processing massive data; To explore the method of reducing the subjective errors in pulse recognition. Methods BP algorithm based on distributed MapReduce in Hadoop environment was optimized. Optimized BP algorithm was used to self-learn pulse-sequence data to reduce fitting errors. The pulse-counting data collected by TCM electronic pulse diagnosis instrument were used as input layer of neural network. Momentum-learning rate adaptive fast BP algorithm was adopted to train neural network. Results In the training set （75%） of 768 M, a total of 35 890 data were collected, and 29 150 items were correctly predicted in stand-alone mode, with the correct rate of 81.22%. MapRedece parallel improved BP algorithm model correctly predicted 35 841 items, with the correct rate of 99.86%. Conclusion Compared with traditional BP algorithm, BP algorithm based on distributed MapReduce in Hadoop environment has smaller fitting errors, with higher accuracy.
出处
《中国中医药信息杂志》
CAS
CSCD
2018年第3期102-106,共5页
Chinese Journal of Information on Traditional Chinese Medicine
基金
国家自然科学基金(81274095)
江苏省自然科学基金青年基金(BK20140958)
