Reduced Kernel Extreme Learning Machine for Traffic Sign Recognition

2019 ◽  
Author(s):  
E. Sanz-Madoz ◽  
J. Echanobe ◽  
O. Mata-Carballeira ◽  
I. del Campo ◽  
M. V. Martinez
Keyword(s):  
Extreme Learning Machine ◽  
Traffic Sign Recognition ◽  
Traffic Sign ◽  
Sign Recognition ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine

Remaining Useful Life Prediction of Lithium-ion Batteries Using Multiple Kernel Extreme Learning Machine

2020 ◽  
Vol 13 ◽  
Author(s):  
Renxiong Liu
Keyword(s):  
Lithium Ion Batteries ◽  
Extreme Learning Machine ◽  
Kernel Function ◽  
Lithium Ion ◽  
Remaining Useful Life ◽  
Prediction Methods ◽  
Multiple Kernel ◽  
Kernel Extreme Learning Machine ◽  
Useful Life ◽  
Learning Machine

Objective: Lithium-ion batteries are important components used in electric automobiles (EVs), fuel cell EVs and other hybrid EVs. Therefore, it is greatly important to discover its remaining useful life (RUL). Methods: In this paper, a battery RUL prediction approach using multiple kernel extreme learning machine (MKELM) is presented. The MKELM’s kernel keeps diversified by consisting multiple kernel functions including Gaussian kernel function, Polynomial kernel function and Sigmoid kernel function, and every kernel function’s weight and parameter are optimized through differential evolution (DE) algorithm. Results : Battery capacity data measured from NASA Ames Prognostics Center are used to demonstrate the prediction procedure of the proposed approach, and the MKELM is compared with other commonly used prediction methods in terms of absolute error, relative accuracy and mean square error. Conclusion: The prediction results prove that the MKELM approach can accurately predict the battery RUL. Furthermore, a compare experiment is executed to validate that the MKELM method is better than other prediction methods in terms of prediction accuracy.

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