基于人工神经网络的摩擦材料性能评价和预测

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 accurate-ly predicting the friction coefficient of friction materials,especially for the formulations with a low usage of abrasives.%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。