基于免疫RBF的列车制动预测模型研究  

作　　者：王娟 朱亚男[1] 

机构地区：[1]西安铁路职业技术学院,陕西西安710014

出　　处：《科技风》2021年第12期105-107,共3页

基　　金：西安铁路职业技术学院2019年度立项课题“智能化单车制动试验台仿真模型研究”(编号:XTZY19G10)。

摘　　要：精确可靠的列车制动预测模型对于列车制动控制系统和列车精确停车控制等应用领域意义重大,由于列车制动过程受线路条件、车型自身参数及外界天气环境等客观因素影响,是一个复杂的非线性系统,因此采用免疫RBF神经网络逼近制动系统模型。本文介绍了免疫RBF网络拓扑结构的构建方法,分析了列车制动过程模型,建立了列车制停的免疫RBF网络,并提取实车运行数据进行特征化处理后训练网络从而实现了列车制动的精准预测。经实车数据仿真实验验证,本文建立的列车制停免疫RBF网络性能优越,预测停车位置与实际值吻合率高达96.3%,具有较大应用价值。Accurate and reliable train braking prediction models are of great significance to application fields such as train braking control systems and train accurate parking control.Because the train braking process is affected by objective factors such as line conditions,vehicle model parameters,and external weather conditions,it is a complex Non-linear system,therefore,an immune RBF neural network is used to approximate the braking system model.This article introduces the construction method of the immune RBF network topology,analyzes the train braking process model,establishes the immune RBF network for train stopping,and extracts the actual train operation data for characterization and then trains the network to realize the train braking precise prediction.The actual vehicle data simulation experiment verifies that the train-stop immune RBF network established in this paper has superior performance,and the predicted parking position coincides with the actual value as high as 96.3%,which has great application value.

关 键 词：人工免疫系统 RBF网络 列车制动 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]