文章摘要
薛继斌,吕亚非,齐士成,江盛玲,张孝阿,员荣平.基于人工神经网络的摩擦材料性能评价和预测[J].润滑与密封,2014,39(11):14-18
基于人工神经网络的摩擦材料性能评价和预测
  
DOI:10.3969/j.issn.0254-0150.2014.11.004
中文关键词: 人工神经网络  摩擦材料  性能预测  摩擦因数
英文关键词: artificial neural network(ANN)  brake friction material  performance prediction  friction coefficient
基金项目:国家自然科学基金项目(50373002;50673012).
作者单位E-mail
薛继斌 北京化工大学 yunrp@mail.buct.edu.cn 
吕亚非 北京化工大学  
齐士成 北京化工大学  
江盛玲 北京化工大学  
张孝阿 北京化工大学  
员荣平 北京化工大学  
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中文摘要:
      基于3种典型的人工神经网络,即Elman(反馈)、BP(前馈)和RBF(径向),分别建立3种制动摩擦材料摩擦性能的评价预测模型,采用[240,8]的数据样本对3种模型进行训练,同时采用贝叶斯正则化训练函数进一步优化。结果表明,Elman网络预测实验数据的精度最高,能较为准确地预测摩擦材料的升温摩擦因数和降温摩擦因数,尤其适用于磨料含量较低的情况。
英文摘要:
      Three different evaluation models on tribolocical performances of brake friction composites were established based on three types of typical artificial neural networks (ANN),including Elman,BP and RBF.All three models were trained and optimized with a Bayesian Regulation algorithm,and were applied to predict the friction coefficient of friction materials in both heating and cooling processes.The research results show that the Elman model is the best one in accurately predicting the friction coefficient of friction materials,especially for the formulations with a low usage of abrasives.
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