基于BP神经网络的测量设备无关协议参数预测  

Measurement Device Independent Protocol Parameter Prediction Based on BP Neural Network

作　　者：周江平[1] 周媛媛[1] 周学军[1] 李洁琼 ZHOU Jiangping;ZHOU Yuanyuan;ZHOU Xuejun;LI Jieqiong(College of Electronic Engineering,Naval University of Engineering,Wuhan 430000,China;Unit 92682,Guangdong 524000,China)

机构地区：[1]海军工程大学电子工程学院,武汉430000 [2]92682部队,湛江524000

出　　处：《电子科技大学学报》2024年第4期611-616,共6页Journal of University of Electronic Science and Technology of China

摘　　要：针对传统参数优化方法计算开销大,不能满足实时性要求高、计算量大等应用场景的问题,结合当今主流的机器学习方法,提出了一种改进的基于BP神经网络的参数优化方法,利用本地搜索算法的数据训练网络并对参数进行预测,替代传统的查找算法,从而获得更好的实时性和更低的计算复杂度,随后与基于随机森林和XGBoost的方法进行了比较。仿真结果表明,BP神经网络预测所得各参数的均方误差数量级为10^(-6)或更小,由该参数计算所得密钥生成率与最优密钥生成率比值的均值为0.998 8,且该应用中BP神经网络相对随机森林和XGBoost具有更好的预测性能。The traditional parameter optimization algorithm cannot meet the requirements of the application scenarios with high real-time,large amount of calculation.Combining with the current mainstream machine learning methods,an improved parameter optimization method based on back-propagation BP neural network is proposed.Using the data of the local search algorithm to train the network and predict the parameters,the proposed methods can obtain better real-time performance and lower computational complexity for the traditional search algorithm is replaced.The proposed method is compared with random forest and XGBoost methods.The simulation results show that the order of magnitude of the mean square error of each parameter predicted by BP 10^(-6) neural network is or less.The average value of the ratio between the key generation rate calculated by the predicted parameter and the optimal key generation rate is 0.9988.And BP neural network in this application has better prediction performance than random forest and XGBoost.

关 键 词：量子光学 量子密钥分发 BP神经网络 参数优化 测量设备无关 

分 类 号：TN918[电子电信—通信与信息系统] O431.2[电子电信—信息与通信工程]