A Novel Improved ELM Algorithm for a Real Industrial Application

作者：Zhang, Hai-Gang; Zhang, Sen^*; Yin, Yi-Xin

来源：Mathematical Problems in Engineering, 2014, 2014: 824765.

DOI：10.1155/2014/824765

• EXTREME LEARNING-MACHINE
• FAULT-DIAGNOSIS
• INDUCTION-MOTOR
• NEURAL-NETWORKS
• CLASSIFICATION
• REGRESSION
• STABILITY

It is well known that the feedforward neural networks meet numbers of difficulties in the applications because of its slow learning speed. The extreme learningmachine (ELM) is a new single hidden layer feedforward neural network method aiming at improving the training speed. Nowadays ELM algorithm has received wide application with its good generalization performance under fast learning speed. However, there are still several problems needed to be solved in ELM. In this paper, a new improved ELM algorithm named R-ELM is proposed to handle the multicollinear problem appearing in calculation of the ELM algorithm. The proposed algorithm is employed in bearing fault detection using stator current monitoring. Simulative results show that R-ELM algorithm has better stability and generalization performance compared with the original ELM and the other neural network methods.

• 出版日期2014
• 单位北京科技大学