摘要
介绍了RBF神经网络的模型结构及其功能特点。根据提升机主轴轴承的磨损烈度监测历史数据,在MatLab环境下创建了RBF神经网络模型,设定各种参数后训练网络。利用训练好的网络实现对下一时刻提升机主轴轴承的磨损参数的预测,根据参数预测结果即可预判提升机下一时刻的工况。通过工程实例验证了该方法的可行性,结果显示本预测方法具有较高的精度和准确性。
The model structure and the functional characteristics of RBF neural network were introduced. Based on the historical data monitored about indexes of wear severity of the bearing of main shaft of hoist,the model of RBF neural network was created using Matlab software and trained after setting various parameters. Then the trained network was used to forecast the wear parameters of the bearing of main shaft at next moment, and the wear condition of the next moment was forecasted according to the prediction results.The forecast method proves feasible with engineering examples,and the results display good accuracy and precision of the forecast method.
出处
《矿山机械》
北大核心
2010年第19期59-63,共5页
Mining & Processing Equipment
