Investigation of mechanical factor of soil reinforced with four types of fibers: An integrated experimental and extreme learning machine approach

2018 ◽  
Vol 17 (5) ◽  
pp. 650-664 ◽  
Author(s):  
Ankit Garg ◽  
Sanandam Bordoloi ◽  
Somnath Mondal ◽  
Jun-Jun Ni ◽  
S Sreedeep
Keyword(s):  
Extreme Learning Machine ◽  
Mechanical Factor ◽  
Learning Machine

Performance of Extreme Learning Machine Kernels in Classifying EEG Signal Pattern of Dyslexic Children in Writing

2019 ◽  
Vol 11 (3) ◽  
Author(s):  
A. Z. Ahmad Zainuddin ◽  
◽  
W. Mansor ◽  
Khuan Y. Lee ◽  
Z. Mahmoodin ◽  
...  
Keyword(s):  
Extreme Learning Machine ◽  
Eeg Signal ◽  
Signal Pattern ◽  
Learning Machine

Model selection of extreme learning machine based on latent feature space

2013 ◽  
Vol 33 (6) ◽  
pp. 1600-1603
Author(s):  
Wentao MAO ◽  
Zhongtang ZHAO ◽  
Huanhuan HE
Keyword(s):  
Model Selection ◽  
Extreme Learning Machine ◽  
Feature Space ◽  
Learning Machine ◽  
Selection Of

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

A Pathological Brain Detection System based on Extreme Learning Machine Optimized by Bat Algorithm

2017 ◽  
Vol 16 (1) ◽  
pp. 23-29 ◽  
Author(s):  
Siyuan Lu ◽  
Xin Qiu ◽  
Jianping Shi ◽  
Na Li ◽  
Zhi-Hai Lu ◽  
...  
Keyword(s):  
Extreme Learning Machine ◽  
Detection System ◽  
Bat Algorithm ◽  
Pathological Brain Detection ◽  
Learning Machine

User Power Behavior Similarity Clustering Based on Unsupervised Extreme Learning Machine Algorithm

2020 ◽  
Vol 13 (5) ◽  
pp. 641-649
Author(s):  
Yuancheng Li ◽  
Yaqi Cui ◽  
Xiaolong Zhang
Keyword(s):  
Extreme Learning Machine ◽  
Clustering Algorithm ◽  
Characteristic Curve ◽  
Clustering Algorithms ◽  
Data Sets ◽  
Residential Areas ◽  
Processing Power ◽  
Learning Machine ◽  
Advanced Metering ◽  
Matlab Programming

Background: Advanced Metering Infrastructure (AMI) for the smart grid is growing rapidly which results in the exponential growth of data collected and transmitted in the device. By clustering this data, it can give the electricity company a better understanding of the personalized and differentiated needs of the user. Objective: The existing clustering algorithms for processing data generally have some problems, such as insufficient data utilization, high computational complexity and low accuracy of behavior recognition. Methods: In order to improve the clustering accuracy, this paper proposes a new clustering method based on the electrical behavior of the user. Starting with the analysis of user load characteristics, the user electricity data samples were constructed. The daily load characteristic curve was extracted through improved extreme learning machine clustering algorithm and effective index criteria. Moreover, clustering analysis was carried out for different users from industrial areas, commercial areas and residential areas. The improved extreme learning machine algorithm, also called Unsupervised Extreme Learning Machine (US-ELM), is an extension and improvement of the original Extreme Learning Machine (ELM), which realizes the unsupervised clustering task on the basis of the original ELM. Results: Four different data sets have been experimented and compared with other commonly used clustering algorithms by MATLAB programming. The experimental results show that the US-ELM algorithm has higher accuracy in processing power data. Conclusion: The unsupervised ELM algorithm can greatly reduce the time consumption and improve the effectiveness of clustering.

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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