Research on Weigh-in-Motion Algorithm of Vehicles Based on BSO-BP

• Author: Xu, Suan ; Chen, Xing ; Fu, Yaqiong ; Xu, Hongwei ; Hong, Kaixing
• Subjects: Accuracy ; Algorithms ; Animals ; Back propagation networks ; BP neural network ; BSO algorithm ; Coleoptera ; Errors ; Fault diagnosis ; Genetic algorithms ; Motion ; Neural networks ; Neural Networks, Computer ; Noise ; Optimization ; Parameter estimation ; Pattern recognition ; Roads & highways ; Sensors ; Signal processing ; Traffic congestion ; Vehicles ; Velocity ; Wavelet Analysis ; wavelet transform ; Wavelet transforms ; Weighing in motion ; WIM
• Is Part Of: Sensors (Basel, Switzerland), 2022-03, Vol.22 (6), p.2109
• Description: Weigh-in-motion (WIM) systems are used to measure the weight of moving vehicles. Aiming at the problem of low accuracy of the WIM system, this paper proposes a WIM model based on the beetle swarm optimization (BSO) algorithm and the error back propagation (BP) neural network. Firstly, the structure and principle of the WIM system used in this paper are analyzed. Secondly, the WIM signal is denoised and reconstructed by wavelet transform. Then, a BP neural network model optimized by BSO algorithm is established to process the WIM signal. Finally, the predictive ability of BP neural network models optimized by different algorithms are compared and conclusions are drawn. The experimental results show that the BSO-BP WIM model has fast convergence speed, high accuracy, the relative error of the maximum gross weight is 1.41%, and the relative error of the maximum axle weight is 6.69%.
  
• Publisher: Switzerland: MDPI AG
  
• Language: English
  
• Identifier: ISSN: 1424-8220
  EISSN: 1424-8220
  DOI: 10.3390/s22062109
  PMID: 35336283
  
• Source: Directory of Open Access Journals - May need to register for free articles
  GFMER Free Medical Journals
  MEDLINE
  PubMed Central
  Coronavirus Research Database
  ROAD: Directory of Open Access Scholarly Resources
  ProQuest Central