Changfan Zhang, Xiang Cheng, Jing He, and Guangwei Liu
Adhesion control, extreme learning machine, automatic recognition,machine learning, neural network
In this study, the use of an extreme learning machine (ELM) for
automatic identiﬁcation of the adhesion state is investigated. The
inﬂuence of diﬀerent activation functions and the number of neurons
in the hidden layers on the recognition performance is investigated.
This study aims to select a better activation function and construct
an approach for the adhesion state recognition by using an ELM.
Monitoring data on the adhesion characteristics of a heavy-duty
locomotive are used to fabricate the recognition model. Comparing
with the backpropagation (BP) neural network and a BP optimized
algorithm, the experimental results show that our ELM recognition
method has a faster training speed and higher recognition accuracy
than the other two approaches.