基于SQPSO优化DELM的踏面磨耗测量模型

针对难以建立轮轨磨耗的单一模型和无法对各种工况下车轮踏面磨耗进行定量计算的问题，提出一种基于SQPSO优化DELM的踏面磨耗测量方法(SQPSO-DELM). 首先将衍生特性引入到极限学习机中，提出一种衍生极限学习机模型(DELM). 然后引入序列二次规划(SQP)方法和量子粒子群优化(QPSO)算法，对DELM的参数进行优化. 通过SQPSO-DELM预测模型，对车辆动力学模型模拟不同试验参数下的车轮踏面最大磨耗量以及对现场列车踏面磨耗程度的实际测量值进行训练和预测. 结果表明:SQPSO-DELM预测模型的性能参数指标均优于LSSVM、ELM、PSO-ELM和QPSO-ELM，能较好地反映不同参数对车轮踏面磨耗值的影响规律. 

Measurement Model of Tread Wear Based on SQPSO Optimized DELM

In view of the difficulty in establishing accurate mathematical model of wheel rail wear and in evaluating, predicting and quantitatively calculating wheel rail wear under various working conditions, this paper proposed a tread wear prediction method based on SQPSO optimized DELM model(SQPSO-DELM). First of all, the derivative characteristics were introduced into the learning machine, and a derivative learning machine model (DELM) was proposed. Then, the sequential quadratic programming (SQP) and quantum particle swarm optimization (QPSO) algorithm were introduced to optimize the parameters of DELM. Through SQPSO-DELM prediction model, the maximum wear of wheel tread under different test parameters of vehicle dynamics model simulation and the actual measured value of wear degree of on-site train tread were trained and predicted. The results showed that the performance parameters of SQPSO-DELM prediction model were better than LSSVM, ELM, PSO-ELM and QPSO-ELM, which can better reflect the influence of different parameters on wheel tread wear value.