一种用于Bi-LSTM神经网络信号识别的DO-CAB算法  

DO-CAB algorithm for Bi-LSTM neural network signal recognition

作　　者：花国祥 汤炼海[3] 李伟伟 李鹏 孙炎 HUA Guoxiang;TANG Lianhai;LIWeiwei;LI Peng;SUN Yan(School of Automation,Wuxi University,Wuxi Jiangsu 214105,China;School of Electrical and Electronic Engineering,North China Electric Power University,Beijing 102206,China;Nanjing University of Information Science&Technology,Nanjing 210044,China)

机构地区：[1]无锡学院自动化学院,江苏无锡214105 [2]华北电力大学电气与电子工程学院,北京102206 [3]南京信息工程大学,南京210044

出　　处：《光通信技术》2024年第6期23-27,共5页Optical Communication Technology

基　　金：新能源电力系统全国重点实验室开放课题项目(LAPS23014)资助。

摘　　要：针对双向工频通信系统(TWACS)存在上行信号识别准确率不足的问题,提出一种基于蒲公英优化(DO)算法的联合卷积神经网络(CNN)与注意力机制(AM)的双向长短时记忆(Bi-LSTM)神经网络信号识别算法,简称DO-CAB算法。该算法首先通过CNN自适应提取TWACS信号重要特征,然后使用DO算法优化Bi-LSTM超参数,根据优化的超参数构建网络,并引入AM赋予输入影响权重,以获得更好信号识别效果。实验结果表明,所提算法的识别准确率达到92.32%,能高效、准确识别TWACS调制信号。To address the problem of insufficient recognition accuracy for uplink signals in two-way automatic communication systems(TWACS),a signal recognition algorithm based on the dandelion optimization(DO)algorithm that combines convolutional neural network(CNN)with attention mechanism(AM)and bidirectional long short-term memory(Bi-LSTM)neural networks is proposed,which is briefly referred to as the DO-CAB algorithm.The algorithm first adaptively extracts important features of TWACS signals using a CNN.It then optimizes the hyperparameters of the Bi-LSTM using the DO algorithm,constructs the network based on the optimized hyperparameters,and introduces an AM to assign influence weights to the inputs,improving the network algorithm for better signal recognition.The experimental results show that the proposed algorithm achieves a recognition accuracy of 92.32%,enabling efficient and accurate identification of TWACS modulated signals.

关 键 词：双向工频通信系统 蒲公英优化算法 双向长短时记忆网络 深度学习 信号检测 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] TN929[自动化与计算机技术—控制科学与工程]