摘要
为提高铁路信号运行状态预警的准确率,克服BPNN模型存在收敛速度慢和局部最优的缺点及其性能易受网络的初始权值、阈值等参数选择的影响,本文提出一种基于CAPSO-BPNN的铁路信号运行状态预警模型。结果表明,与PSO-BPNN和BPNN相比较,CAPSO-BPNN模型具有更高的预警准确率和更优的性能,为铁路信号运行状态预警提供了新的方法和途径。
In order to improve the precautionary accuracy for railway signal running state and overcome disadvantages of slow convergence speed and local optimization in BPNN model,and its performance was easily affected by the initial weight value and threshold value and so on,this paper proposed a precautionary model based on CAPSO-BPNN for railway signal running state.The results showed that the CAPSO-BPNN model had higher accuracy and better performance than PSO-BPNN and BPNN,which provided new method and approach for forewarning of the operating state of railway signals.
作者
喻喜平
YU Xi-ping(Wuhan Railway Vocational College of Technology,Wuhan 430205,China)
出处
《山东农业大学学报(自然科学版)》
北大核心
2019年第2期281-284,共4页
Journal of Shandong Agricultural University:Natural Science Edition
关键词
铁路信号
云自适应粒子群优化算法
BP神经网络
运行状态
Railway signal
cloud adaptive particle swarm optimization(CAPSO)
BP neural network(BPNN)
running state