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

Abstract

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

CLC Number:

TN92

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.

Figures/Tables 8

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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

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

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