Application Research on BP Algorithm in Pulse Recognition Based on Hadoop Environment

VernacularTitle:基于Hadoop环境BP改进算法的脉象识别应用研究
Author: Ya-Lan SHENG ¹ ; Zhen WANG ; Kan-Kan SHE
Author Information

1. 南京中医药大学信息技术学院
Keywords: Hadoop; MapReduce; BP algorithm; pulse recognition
From: Chinese Journal of Information on Traditional Chinese Medicine 2018;25(3):102-106
CountryChina
Language:Chinese
Abstract: 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.