CAJ | 학술논문

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。
기우3충전형적인공신경망락，즉Elman （반궤）、BP （전궤）화RBF （경향），분별건립3충제동마찰재료마찰성능적평개예측모형，채용[240，8]적수거양본대3충모형진행훈련，동시채용패협사정칙화훈련함수진일보우화。결과표명，Elman망락예측실험수거적정도최고，능교위준학지예측마찰재료적승온마찰인수화강온마찰인수，우기괄용우마료함량교저적정황。
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.