电子元件与材料2025，Vol.44Issue(7)：774-781,8.DOI:10.14106/j.cnki.1001-2028.2025.0104
基于改进粒子群算法的路面水膜厚度传感器的研究
Pavement water film thickness sensor based on improved particle swarm optimization algorithm
摘要
Abstract
In order to address temperature drift and salinity-induced phase-amplitude shifts in water film thickness(WFT)sensors,a pavement WFT prediction model was developed by integrating an improved particle swarm optimization(IMPSO)algorithm with a backpropagation neural network(BPNN).Nonlinear inertia weights and adaptive learning factors into the traditional particle swarm optimization(PSO)framework were introduced in the IMPSO algorithm,and the premature convergence to local optima was effectively prevented.The initial weights and thresholds of the BPNN were optimized,slow convergence and local minima trapping typically encountered with BPNN were overcome.The experimental results show that the IMPSO-BPNN model outperforms the genetic algorithm-optimized BPNN(GA-BP)and PSO-optimized BPNN(PSO-BP)in terms of generalization ability,and a root mean square error(RMSE)of 0.3043 mm,a mean absolute error(MAE)of 0.2256 mm,and a goodness of fit(R2)of 0.9824 were achieved.With the prediction errors within±0.5 mm,an accuracy of 93.52％was achieved.The experimental validation confirms the high precision and strong robustness of the model in pavement WFT measurement,providing a reliable theoretical and technical foundation for application in practical engineering.关键词
神经网络/粒子群优化/数据融合/路面检测Key words
neural networks/particle swarm optimization/data fusion/pavement monitoring分类
信息技术与安全科学引用本文复制引用
王焕章,许高斌,蒋京奇..基于改进粒子群算法的路面水膜厚度传感器的研究[J].电子元件与材料,2025,44(7):774-781,8.基金项目
国家重点研发计划(2022YFB3205903) （2022YFB3205903）
