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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。
Introduced the RBF neural network model structure and its features. According to the monitoring data of hoisting spindle bearing wear intensity, a RBF neural network model was created under MatLab environment, and various parameters were set to train the network. Using the trained network to predict the wear parameters of the hoist spindle bearing at the next moment, the working condition of the hoisting machine at the next moment can be predicted according to the prediction results of the parameters. The feasibility of this method is verified by the engineering example. The result shows that this method has higher accuracy and accuracy.