一种基于BP神经网络的车载通信设备性能评估方法

doi:10.16180/j.cnki.issn1007-7820.2021.05.005

电子科技 ›› 2021, Vol. 34 ›› Issue (5): 24-28.doi: 10.16180/j.cnki.issn1007-7820.2021.05.005

一种基于BP神经网络的车载通信设备性能评估方法

孟晓姣¹,张世巍²,李小健²,李敏玥¹,宋丙鑫¹,路宏敏¹

1.beoplay体育提现电子工程学院,陕西西安 710071
2.中国北方车辆研究所电磁兼容实验室,北京 100072

收稿日期:2020-01-13 出版日期:2021-05-15 发布日期:2021-05-24
作者简介:孟晓姣(1993-),女,硕士研究生。研究方向:电子与通信工程。|李敏玥(1995-),女,硕士研究生。研究方向:环境工程。|路宏敏(1961-),男,博士,教授,博士生导师。研究方向:电磁场与微波技术、工程电磁兼容、环境科学。
基金资助:
国防预研项目(JZX7X201901JY0048)

An Evaluation Method of Vehicle-Mounted Communication Equipment Performance Based on BP Neural Network

MENG Xiaojiao¹,ZHANG Shiwei²,LI Xiaojian²,LI Minyue¹,SONG Bingxin¹,LU Hongmin¹

1. School of Electronic Engineering,Xidian University,Xi’an 710071,China
2. EMC Laboratory,China North Vehicle Research Institute,Beijing 100072,China

Received:2020-01-13 Online:2021-05-15 Published:2021-05-24
Supported by:
The Defense Advanced Research Projects(JZX7X201901JY0048)

摘要/Abstract

摘要：

不断增多的车载通信设备数量导致车载通信系统面临日益严重的电磁干扰问题。针对复杂电磁环境下车载通信设备性能评估问题,文中基于神经网络非线性拟合精度高及自调节功能强的特点,提出了一种车载通信设备性能评估方法。依据车载通信设备的关键技术指标,建立了发射、传输、接收的链路评估体系,构建了基于BP神经网络的车载通信设备性能评估模型。利用MTALAB使用大量数据样本优化训练BP神经网络模型结构,提高了评估模型精度。验证结果表明,所构建神经网络评估模型归一化均方误差可达-36 dB,且评估误差较小。

关键词: 车载通信设备, 通信性能, BP神经网络, 通信距离, 评估模型, 模型训练, NMSE, 评估精度

Abstract:

With an increasing number of vehicular communication equipment, the communication system is faced with increasingly serious electromagnetic interference problem. In this study, based on the characteristics of high nonlinear fitting accuracy and strong self-tuning of BP neural network, an evaluation method is proposed to evaluate the performance of vehicular communication equipment under the complex electromagnetic environment. According to the critical technology indicators of vehicular communication equipment, an evaluation system including transmission, reception and interaction is established, and an evaluation model based on BP neural network structure is constructed. With the use of MTALAB software, large amounts of sample data are adopted to train and optimize the BP neural network model structure, and to improve the evaluating model accuracy. The validation results indicate that the normalized mean square error of the model reaches -36 dB, and the evaluation error is small.

Key words: vehicle-mounted communication equipment, communication performance, BP neural network, communication distance, evaluation model, model training, NMSE, evaluation accuracy

中图分类号:

TN92

孟晓姣,张世巍,李小健,李敏玥,宋丙鑫,路宏敏. 一种基于BP神经网络的车载通信设备性能评估方法[J]. 电子科技, 2021, 34(5): 24-28.

MENG Xiaojiao,ZHANG Shiwei,LI Xiaojian,LI Minyue,SONG Bingxin,LU Hongmin. An Evaluation Method of Vehicle-Mounted Communication Equipment Performance Based on BP Neural Network[J]. Electronic Science and Technology, 2021, 34(5): 24-28.

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样本	样本数据	模型输出	误差
1	0.317	0.314 4	0.002 6
2	0.235	0.230 2	0.004 8
3	0.254	0.273 4	0.019 4
4	0.441	0.439 9	0.001 1
5	0.414	0.410 6	0.003 4
6	0.493	0.490 4	0.002 6
7	0.539	0.540 0	0.001 0
8	0.360	0.361 0	0.001 0
9	0.426	0.430 1	0.004 1
10	0.677	0.680 1	0.003 1

一种基于BP神经网络的车载通信设备性能评估方法

An Evaluation Method of Vehicle-Mounted Communication Equipment Performance Based on BP Neural Network

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