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作 者:梁文祯[1] 郑则炯[2] 张华[3] LIANG Wenzhen;ZHENG Zejiong;ZHANG Hua(Guangdong Polytechnic of Water Resources and Electric Engineering,Guangzhou 510925,China;School of Mechatronics,Guangdong Industry Polytechnic,Guangzhou,510300,China;College of Automation,Guangdong Polytechnic Normal University,Guangzhou 510635,China)
机构地区:[1]广东水利电力职业技术学院,广州510925 [2]广东轻工职业技术学院机电技术学院,广州510300 [3]广东技术师范学院自动化学院,广州510635
出 处:《激光杂志》2018年第8期71-75,共5页Laser Journal
基 金:广东省青年人才培养基金(No.5411237)
摘 要:车载光通信网络受到车辆之间的串扰影响容易出现失真,为了提高车载光通信网络的数据转发准确性,改善通信质量,提出基于改进BP网络的车载光通信网络路由设计方法。构建车载光通信网络的传输链路模型,采用改进的自组织BP神经网络进行路由节点的自适应寻优,实现路由节点优化部署。以传递延迟、误比特率和节点适应度为约束指标参量,构建车载光通信网络路由控制目标函数,采用无监督学习算法进行最佳参量寻优,设计车载光通信网络的反演控制协议,实现网络路由优化设计。仿真结果表明,采用该方法进行车载光通信网络路由设计,提高数据转发的准确概率,通信网络的误比特率较低,对网络通信质量改善较为明显。The on-board optical communication network influenced by crosstalk between vehicles was prone to distortion,in order to improve the accuracy of vehicle transmitting optical communication network data and the quality of the communication,we put forward an on-board optical communication network routing design method based on the improved BP network. Transmission link model of vehicle's optical communication network,using the improved adaptive routing node self-organizing BP neural network to routing optimization,realize the optimal deployment of nodes. Treat transfer delay,bit error rate and node fitness as the constraint index parameter,set up a vehicle optical communication network routing control objective function,use unsupervised learning algorithm for optimum parameter optimization,design inverse control protocol for vehicle optical communication network,realize network routing optimization.The simulation results show that using the method of design of vehicle routing in optical communication network,improve the probability of accurate data forwarding.The bit error rate of the network is low,and the quality of network communication is improved obviously.
分 类 号:TN911[电子电信—通信与信息系统]
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