Fault Diagnosis of Charging Piles by Fusing Gradient Boosting Decision Tree and Perceptron
In order to solve the common problems of feature extraction and the over-fitting of complex artificial intel-ligence models for fault diagnosis of charging piles,a new method based on gradient boosting decision tree and mul-ti-layer perceptron fusion is proposed in this paper.According to the ensemble learning theory,multiple independent models based on gradient boosting decision tree are established,which are used to construct multiple fully connected layers of gradient boosting decision tree,and finally a multi-layer perceptron is arranged at the end of the network for feature learning and classification.This fusion scheme avoids the difficulty on manually extracting features.More-over,while reducing the dependence on the performance and complexity of an individual model,the fusion perfor-mance is still improved and more stable.Experimental results on the public dataset show that the proposed scheme is superior to typical individual machine learning schemes.
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