| 陈亮,张媛媛,刘韵婷.基于改进的LSTM的药品温湿度预测方法[J].电子测量与仪器学报,2019,33(1):106-112 |
| 基于改进的LSTM的药品温湿度预测方法 |
| Method for predicting temperature and humidity of medicine based on improved LSTM |
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| DOI： |
| 中文关键词: 医药冷链 循环神经网络 长短期记忆模型 温湿度预测 深度学习 |
| 英文关键词:pharmaceutical cold chain recurrent neural network long short term memory model temperature and humidity prediction deep learning |
| 基金项目:辽宁省教育厅基本科研项目（LG201707）、辽宁省自然科学基金项目（20170540788）、国家重点研发计划（2017YFC0821001，2017YFC0821004）资助 |
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| 中文摘要: |
| 针对医药冷链系统中药品温湿度数据不易诊断的问题，提出一种改进的长短期记忆(LSTM)预测药品温湿度的方法。首先通过插值扩充算法扩充湿度数据集，接着提出一种内含多个LSTM细胞元的LSTM结构，代替传统的迭代预测，随后通过Adam优化算法调整网络参数和改变网络层数降低预测误差，实现对药品温湿度的提前预判。最后在药店冷藏柜中采集到的药品温湿度数据集上进行测试，均方误差(MSE)为0036 9。与传统的BP神经网络预测方法和高斯过程混合模型预测方法对比，改进的LSTM药品温湿度预测方法预测更准确。 |
| 英文摘要: |
| Aiming at the problem that the temperature and humidity data of medicines in medical cold chain system are not easy to diagnose, an improved long short term memory（LSTM） method for predicting the temperature and humidity of drugs is proposed. The method first expands the humidity data set by interpolation expansion algorithm, and then proposes an LSTM structure containing multiple LSTM cell elements instead of the traditional iterative prediction. Then the Adam optimization algorithm adjusts the network parameters and changes the network layer to reduce the prediction error. Achieve early prediction of the temperature and humidity of the drug. Finally, the test was carried out on the temperature and humidity data set of the drug collected in the pharmacy refrigerator. The mean square error was 0036 9. Compared with the traditional BP neural network prediction method and the Gaussian process mixture model prediction method, the improved LSTM drug temperature and humidity prediction method is more accurate. |
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