文章基本信息

标题：Optimization of LMBP high-speed railway wheel size prediction algorithm based on improved adaptive differential evolution algorithm
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作者：Yu Zhang ; Jiawen Zhang ; Lin Luo 等
期刊名称：International Journal of Distributed Sensor Networks
印刷版ISSN：1550-1329
电子版ISSN：1550-1477
出版年度：2019
卷号：15
期号：10
页码：1
DOI：10.1177/1550147719881348
出版社：Hindawi Publishing Corporation
摘要：It is beneficial for maintenance department to make maintenance strategy and reduce maintenance cost to forecast the hidden danger index value. Based on the analysis of the research status of wheel-to-life prediction at home and abroad and the repair of wheel-set wear and tear, this article designs and implements an adaptive differential evolution algorithm Levenberg–Marquardt back propagation wheel-set size prediction model. Aiming at the shortcomings of back propagation neural network, it is easy to fall into local extreme value. The back propagation algorithm is improved by Levenberg–Marquardt numerical optimization algorithm. Aiming at the shortcomings of back propagation neural network algorithm for randomly initializing connection weights and thresholds to fall into local extreme value, the differential evolution algorithm is used to optimize the initial connection weights and thresholds between the layers of the neural network. In order to speed up the search of the optimal initial weights and thresholds of the differential evolution algorithm Levenberg–Marquardt back propagation neural network, the initial values are further optimized, and an adaptive differential evolution algorithm Levenberg–Marquardt back propagation wheel-set size prediction model is designed and implemented. Compared with the proposed combine adaptive differential evolution algorithm with LMBP optimization (ADE-LMBP) is effective and significantly improves the prediction accuracy.
关键词：Wheel-set size prediction; neural network model; Levenberg–Marquardt algorithm; differential evolution algorithms
其他关键词：Wheel-set size prediction ; neural network model ; Levenberg–Marquardt algorithm ; differential evolution algorithms