传感技术学报2018，Vol.31Issue(3)：408-414,7.DOI:10.3969/j.issn.1004-1699.2018.03.015
基于AP聚类RBF神经网络的改进算法及试验
Improved Algorithm and Experiment of RBF Neural Network Based on AP Clustering
摘要
Abstract
An improved algorithm is proposed to solve the lower application precision of radical basis function (RBF)neural network based on affinity propagation(AP)clustering on the vehicle weigh-in-motion. This algorithm takes RBF neural network test error as the criteria to increase iteratively the preference which is obtained with fixed step length. In this way,appropriate hidden layer nodes are obtained. Classification and interpolation analysis of test sample is carried out based on the actual connection weight of two training samples which are nearest between the exemplar and the test sample,making the connection weight can be adjusted adaptively with the test sample. Five vehicles with different loads are considered in the actual engineering test when the vehicle speed is ranged from 10 km/h to 50 km/h while the temperature shifts from 16℃ to 29℃.According to 500 cycle tests,the RBF neural network model of vehicle weigh-in-motion is constructed.The experiment results show that the averaged weighing er-ror of the proposed algorithm is less than 0.06% and the averaged value of the maximum real-time is 0.022 3,meet-ing effectively the practical engineering requirements.关键词
AP聚类/RBF神经网络/动态称重/偏向参数/连接权值/实时性Key words
AP clustering/RBF neural network/weigh-in-motion/preference/connection weight/real-time分类
信息技术与安全科学引用本文复制引用
刘小锋,冯志敏,陈跃华,张刚,李宏伟..基于AP聚类RBF神经网络的改进算法及试验[J].传感技术学报,2018,31(3):408-414,7.基金项目
国家自然科学基金项目(51675286) （51675286）
