Stay one forget multiple extreme learning machine with deep network using time interval process: A review

2017 ◽  
Author(s):  
Agrata Shukla ◽  
Vijay Bhandari ◽  
Amit Shrivastava
Keyword(s):  
Extreme Learning Machine ◽  
Time Interval ◽  
Deep Network ◽  
Learning Machine

Lithium-ion battery state of health estimation based on improved deep extreme learning machine

2021 ◽  
pp. 1-12
Author(s):  
Yu Zhang ◽  
Wanwan Zeng ◽  
Chun Chang ◽  
Qiyue Wang ◽  
Si Xu
Keyword(s):  
Extreme Learning Machine ◽  
Search Algorithm ◽  
Lithium Ion ◽  
Accurate Estimation ◽  
Time Interval ◽  
State Of Health ◽  
Discharge Data ◽  
Ergodic Method ◽  
Discharge Temperature ◽  
Learning Machine

Abstract Accurate estimation of the state of health (SOH) is an important guarantee for safe and reliable battery operation. In this paper, an online method based on indirect health features (IHF) and sparrow search algorithm fused with deep extreme learning machine (SSA-DELM) of lithium-ion batteries is proposed to estimate SOH. Firstly, the temperature and voltage curves in the battery discharge data are acquired, and the optimal intervals are obtained by ergodic method. Discharge temperature difference at equal time intervals (DTD-ETI) and discharge time interval with equal voltage difference (DTI-EVD) are extracted as IHF. Then, the input weights and hidden layer thresholds of the DELM algorithm are optimized using SSA, and the SSA-DELM model is applied to the estimation of battery's SOH. Finally, the established model is experimentally validated using the battery data, and the results show that the method has high prediction accuracy, strong algorithmic stability and good adaptability.

scholarly journals Deep Network Based on Stacked Orthogonal Convex Incremental ELM Autoencoders

2016 ◽  
Vol 2016 ◽  
pp. 1-17 ◽  
Author(s):  
Chao Wang ◽  
Jianhui Wang ◽  
Shusheng Gu
Keyword(s):  
Least Squares ◽  
Extreme Learning Machine ◽  
Incremental Learning ◽  
Learning Algorithm ◽  
Complex Data ◽  
Classification Problems ◽  
Deep Network ◽  
Least Squares Solution ◽  
Fast Learning ◽  
Learning Machine

Extreme learning machine (ELM) as an emerging technology has recently attracted many researchers’ interest due to its fast learning speed and state-of-the-art generalization ability in the implementation. Meanwhile, the incremental extreme learning machine (I-ELM) based on incremental learning algorithm was proposed which outperforms many popular learning algorithms. However, the incremental algorithms with ELM do not recalculate the output weights of all the existing nodes when a new node is added and cannot obtain the least-squares solution of output weight vectors. In this paper, we propose orthogonal convex incremental learning machine (OCI-ELM) with Gram-Schmidt orthogonalization method and Barron’s convex optimization learning method to solve the nonconvex optimization problem and least-squares solution problem, and then we give the rigorous proofs in theory. Moreover, in this paper, we propose a deep architecture based on stacked OCI-ELM autoencoders according to stacked generalization philosophy for solving large and complex data problems. The experimental results verified with both UCI datasets and large datasets demonstrate that the deep network based on stacked OCI-ELM autoencoders (DOC-IELM-AEs) outperforms the other methods mentioned in the paper with better performance on regression and classification problems.

Sistema Híbrido Baseado Em Extreme Learning Machine E Harmony Search

2016 ◽  
Author(s):  
Edgar Wellington Marques de Almeida ◽  
Mêuser Jorge da Silva Valença
Keyword(s):  
Extreme Learning Machine ◽  
Harmony Search ◽  
Learning Machine
Sign in / Sign up

Export Citation Format

Share Document