Results 1 to 10 of about 599,706 (282)

Evolutionary Voting‐Based Extreme Learning Machines

Mathematical Problems in Engineering, 2014
Voting‐based extreme learning machine (V‐ELM) was proposed to improve learning efficiency where majority voting was employed. V‐ELM assumes that all individual classifiers contribute equally to the decision ensemble. However, in many real‐world scenarios, this assumption does not work well.
Nan Liu, Jiuwen Cao, Zhiping Lin, Pin Pin Pek, Zhi Xiong Koh, Marcus Eng Hock Ong   +5 more
openaire   +1 more source

An Algorithm, Based on Extreme Machine Learning, for Modeling Rate of Material Transfer in EDC Process [PDF]

فناوری در مهندسی هوافضا, 2019
In this paper, Extreme Learning Machine method is used to model the rate of material transfer as an effective parameter in process speed and surface quality.
Moohamadreza Maraki, Hadi Tagimalek, Majid Azargoman, Ramin Karami   +3 more
doaj  

Robust kernel-based model reference adaptive control for unstable aircraft

Advances in Mechanical Engineering, 2016
In this article, a robust kernel-based model reference adaptive control is proposed for an unstable nonlinear aircraft. The heart of the proposed kernel-based model reference adaptive control scheme comprises an offline neural identifier and an online ...
Zhao-Xu Yang, Guang-She Zhao, Rong-Jing Bao, Hai-Jun Rong, Lei-Tao Gao   +4 more
doaj   +1 more source

A Fault Diagnosis Method of Rolling Bearing Based on Attention Entropy and Adaptive Deep Kernel Extreme Learning Machine

Energies, 2022
To address the difficulty of early fault diagnosis of rolling bearings, this paper proposes a rolling bearing diagnosis method by combining the attention entropy and adaptive deep kernel extreme learning machine (ADKELM).
Weiyu Wang, Xunxin Zhao, Lijun Luo, Pei Zhang, Fan Mo, Fei Chen, Diyi Chen, Fengjiao Wu, Bin Wang   +8 more
doaj   +1 more source

Extreme Learning Machine for Multi-Label Classification

Entropy, 2016
Extreme learning machine (ELM) techniques have received considerable attention in the computational intelligence and machine learning communities because of the significantly low computational time required for training new classifiers.
Xia Sun, Jingting Xu, Changmeng Jiang, Jun Feng, Su-Shing Chen, Feijuan He   +5 more
doaj   +1 more source

Illuminance prediction through Extreme Learning Machines

2012 IEEE Workshop on Environmental Energy and Structural Monitoring Systems (EESMS), 2012
Planning, managing, and operating power grids using mixed traditional and renewable energy sources requires a reliable forecasting of the contribution of the renewable sources, due to their variable nature. Besides, the short-term prediction of the climatic conditions finds application in other fields (e.g., Climate Sensitive Buildings). In particular,
S. Ferrari, M. Lazzaroni, V. Piuri, A. Salman, L. Cristaldi, M. Rossi, T. Poli   +6 more
openaire   +4 more sources

A Fast Reduced Kernel Extreme Learning Machine [PDF]

Neural Networks, 2016
In this paper, we present a fast and accurate kernel-based supervised algorithm referred to as the Reduced Kernel Extreme Learning Machine (RKELM). In contrast to the work on Support Vector Machine (SVM) or Least Square SVM (LS-SVM), which identifies the support vectors or weight vectors iteratively, the proposed RKELM randomly selects a subset of the ...
Deng, Wan-Yu, Ong, Yew-Soon, Zheng, Qing-Hua   +2 more
openaire   +3 more sources

Deep information-extreme machine learning for autonomous UAV based on decursive data structure for semantic segmentation of digital image of a region

Радіоелектронні і комп'ютерні системи
The subject of the research is functional categorical models of deep information-extreme machine learning based on linear and hierarchical data structures, methods for optimizing machine learning parameters based on information criteria and constructing ...
Valerii Cheranovskyi, Mykyta Myronenko, Serhii Kovalevskyi, Roman Kraskovskyi, Mykhailo Otroshchenko   +4 more
doaj   +1 more source

R-ELMNet: Regularized extreme learning machine network

Neural Networks, 2020
Principal component analysis network (PCANet), as an unsupervised shallow network, demonstrates noticeable effectiveness on datasets of various volumes. It carries a two-layer convolution with PCA as filter learning method, followed by a block-wise histogram post-processing stage. Following the structure of PCANet, extreme learning machine auto-encoder
Guanghao Zhang, Yue Li, Dongshun Cui, Shangbo Mao, Guang-Bin Huang   +4 more
openaire   +4 more sources

Globality-Locality Preserving Maximum Variance Extreme Learning Machine

Complexity, 2019
An extreme learning machine (ELM) is a useful technique for machine learning; however, the existing extreme learning machine methods cannot exploit the geometric structure information or discriminate information of the data space well.
Yonghe Chu, Hongfei Lin, Liang Yang, Yufeng Diao, Dongyu Zhang, Shaowu Zhang, Xiaochao Fan, Chen Shen, Deqin Yan   +8 more
doaj   +1 more source