Generalized extreme learning machine autoencoder and a new deep neural network

2017 ◽  
Vol 230 ◽  
pp. 374-381 ◽  
Author(s):  
Kai Sun ◽  
Jiangshe Zhang ◽  
Chunxia Zhang ◽  
Junying Hu
Keyword(s):  
Neural Network ◽  
Extreme Learning Machine ◽  
Deep Neural Network ◽  
Learning Machine

scholarly journals Data-Driven Approach for Rainfall-Runoff Modelling Using Equilibrium Optimizer Coupled Extreme Learning Machine and Deep Neural Network

2021 ◽  
Vol 11 (13) ◽  
pp. 6238
Author(s):  
Bishwajit Roy ◽  
Maheshwari Prasad Singh ◽  
Mosbeh R. Kaloop ◽  
Deepak Kumar ◽  
Jong-Wan Hu ◽  
...  
Keyword(s):  
Neural Network ◽  
Extreme Learning Machine ◽  
Deep Neural Network ◽  
Optimal Number ◽  
Gradient Boosting ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Rainfall Runoff ◽  
Hydrological Variable ◽  
Learning Machine

Rainfall-runoff (R-R) modelling is used to study the runoff generation of a catchment. The quantity or rate of change measure of the hydrological variable, called runoff, is important for environmental scientists to accomplish water-related planning and design. This paper proposes (i) an integrated model namely EO-ELM (an integration of equilibrium optimizer (EO) and extreme learning machine (ELM)) and (ii) a deep neural network (DNN) for one day-ahead R-R modelling. The proposed R-R models are validated at two different benchmark stations of the catchments, namely river Teifi at Glanteifi and river Fal at Tregony in the UK. Firstly, a partial autocorrelation function (PACF) is used for optimal number of lag inputs to deploy the proposed models. Six other well-known machine learning models, called ELM, kernel ELM (KELM), and particle swarm optimization-based ELM (PSO-ELM), support vector regression (SVR), artificial neural network (ANN) and gradient boosting machine (GBM) are utilized to validate the two proposed models in terms of prediction efficiency. Furthermore, to increase the performance of the proposed models, paper utilizes a discrete wavelet-based data pre-processing technique is applied in rainfall and runoff data. The performance of wavelet-based EO-ELM and DNN are compared with wavelet-based ELM (WELM), KELM (WKELM), PSO-ELM (WPSO-ELM), SVR (WSVR), ANN (WANN) and GBM (WGBM). An uncertainty analysis and two-tailed t-test are carried out to ensure the trustworthiness and efficacy of the proposed models. The experimental results for two different time series datasets show that the EO-ELM performs better in an optimal number of lags than the others. In the case of wavelet-based daily R-R modelling, proposed models performed better and showed robustness compared to other models used. Therefore, this paper shows the efficient applicability of EO-ELM and DNN in R-R modelling that may be used in the hydrological modelling field.

scholarly journals Label Rectification Learning through Kernel Extreme Learning Machine

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Qiang Cai ◽  
Fenghai Li ◽  
Yifan Chen ◽  
Haisheng Li ◽  
Jian Cao ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Extreme Learning Machine ◽  
Classification Performance ◽  
Considerable Progress ◽  
Strong Representation ◽  
Kernel Extreme Learning Machine ◽  
Classification Tasks ◽  
Learning Machine

Along with the strong representation of the convolutional neural network (CNN), image classification tasks have achieved considerable progress. However, majority of works focus on designing complicated and redundant architectures for extracting informative features to improve classification performance. In this study, we concentrate on rectifying the incomplete outputs of CNN. To be concrete, we propose an innovative image classification method based on Label Rectification Learning (LRL) through kernel extreme learning machine (KELM). It mainly consists of two steps: (1) preclassification, extracting incomplete labels through a pretrained CNN, and (2) label rectification, rectifying the generated incomplete labels by the KELM to obtain the rectified labels. Experiments conducted on publicly available datasets demonstrate the effectiveness of our method. Notably, our method is extensible which can be easily integrated with off-the-shelf networks for improving performance.

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