A Review of Advances in Extreme Learning Machine Techniques and Its Applications

Alade, Oyekale Abel; Selamat, Ali; Sallehuddin, Roselina

doi:10.1007/978-3-319-59427-9_91

Cited by 21 publications

(10 citation statements)

References 32 publications

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“…( 9) for the ANN model development) are de ned randomly, whereas the hidden-output weights and biases are computed analytically [67, 69]. Thus, developing the ELM requires, in practice, lower computational efforts while assuring a better generalization capability as veri ed in various engineering applications compared to the BP-ANN [70][71][72][73].…”

Section: Arti Cial Neural Networkmentioning

confidence: 99%

WITHDRAWN: A prediction model for a concentrating solar thermoelectric generator using artificial neural networks and extreme learning machines

Maduabuchi

Al‐Dahidi

Alnami

et al. 2022

Preprint

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The current numerical simulation tools used to optimize the performance of concentrating solar thermoelectric generators are extremely time consuming, and consequently require expensive computational energies. Furthermore, they are incapable of considering the effects of diverse real-life operating conditions on the performance of the system. Additionally, they sometimes neglect temperature dependency in the thermoelectric semiconductors and base their studies on just unicouple thermoelectric cells to avoid the further complexity of the numerical computation. These factors limit the flexibility of optimization studies that can be conducted on solar thermoelectrics; hence, limiting the insights that can be drawn to design high performing solar thermoelectric generators. This work is the first of its kind to introduce artificial neural networks and extreme learning machines as a substitute to these numerical methods to accelerate and ease the design process of solar thermoelectric generators. The data generation process is conducted using a 3-dimensional numerical model developed in ANSYS numerical solver and the optimized parameters include the high-temperature material content, semiconductor height and area, concentrated solar irradiance, cooling film coefficient, wind speed, and ambient temperature – on the system performance. A full-scale customized thermoelectric module comprising 127 thermocouples is designed and integrated in an optical concentrator for solar power generation while considering temperature dependency in all thermoelectric materials. Results depict that the geometry and operating condition optimization improved the system power and efficiency by 42.02% and 82.23%, respectively. Furthermore, the artificial neural network had the highest regression of 95.82% with the least mean squared error of 2.71 \(\times\) 10− 5 in learning the numerical-generated data set while performing 389 and 203 times faster than the numerical method in forecasting the system power and efficiency, respectively. Finally, methods of manufacturing the optimized thermoelectric module using 3-dimensional printing are discussed.

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Section: Arti Cial Neural Networkmentioning

confidence: 99%

WITHDRAWN: A prediction model for a concentrating solar thermoelectric generator using artificial neural networks and extreme learning machines

Maduabuchi

Al‐Dahidi

Alnami

et al. 2022

Preprint

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“…That means a lot of neurons have a very small effect on the performance of the network. At the same time, the authors proved that the optimal hidden layer parameters normalbold a * , b * can make network output error decrease with the fastest speed (Alade et al, 2017). If we can obtain these two optimal parameters, we can greatly improve the learning speed of EM-ELM.…”

Section: The Preliminariesmentioning

confidence: 99%

Artificial bee colony algorithm–optimized error minimized extreme learning machine and its application in short-term wind speed prediction

Tian

Wang

et al. 2018

Wind Engineering

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In order to improve the prediction accuracy of short-term wind speed, a short-term wind speed prediction model based on artificial bee colony algorithm optimized error minimized extreme learning machine model is proposed. The extreme learning machine has the advantages of fast learning speed and strong generalization ability. But many useless neurons of incremental extreme learning machine have little influences on the final output and, at the same time, reduce the efficiency of the algorithm. The optimal parameters of the hidden layer nodes will make network output error of incremental extreme learning machine decrease with fast speed. Based on the error minimized extreme learning machine, artificial bee colony algorithm is introduced to optimize the parameters of the hidden layer nodes, decrease the number of useless neurons, reduce training and prediction error, achieve the goal of reducing the network complexity, and improve the efficiency of the algorithm. The error minimized extreme learning machine prediction model is constructed with the obtained optimal parameters. The stability and convergence property of artificial bee colony algorithm optimized error minimized extreme learning machine model are proved. The practical short-term wind speed time series is used as the research object and to verify the validity of the prediction model. Multi-step prediction simulation of short-term wind speed is carried out. Compared with other prediction models, simulation results show that the prediction model proposed in this article reduces the training time of the prediction model and decreases the number of hidden layer nodes. The prediction model has higher prediction accuracy and reliability performance, meanwhile improves the performance indicators.

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“…In addition to being used for traditional classification and regression tasks, ELM has recently been extended for clustering, feature selection, and representation learning [ 16 ]. For more research on ELM, please refer to related literatures [ 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 ].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Method Based on Extreme Learning Machine and Wavelet Transform Denoising for Stock Prediction

Wang

2021

Entropy

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The trend prediction of the stock is a main challenge. Accidental factors often lead to short-term sharp fluctuations in stock markets, deviating from the original normal trend. The short-term fluctuation of stock price has high noise, which is not conducive to the prediction of stock trends. Therefore, we used discrete wavelet transform (DWT)-based denoising to denoise stock data. Denoising the stock data assisted us to eliminate the influences of short-term random events on the continuous trend of the stock. The denoised data showed more stable trend characteristics and smoothness. Extreme learning machine (ELM) is one of the effective training algorithms for fully connected single-hidden-layer feedforward neural networks (SLFNs), which possesses the advantages of fast convergence, unique results, and it does not converge to a local minimum. Therefore, this paper proposed a combination of ELM- and DWT-based denoising to predict the trend of stocks. The proposed method was used to predict the trend of 400 stocks in China. The prediction results of the proposed method are a good proof of the efficacy of DWT-based denoising for stock trends, and showed an excellent performance compared to 12 machine learning algorithms (e.g., recurrent neural network (RNN) and long short-term memory (LSTM)).

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A Review of Advances in Extreme Learning Machine Techniques and Its Applications

Cited by 21 publications

References 32 publications

WITHDRAWN: A prediction model for a concentrating solar thermoelectric generator using artificial neural networks and extreme learning machines

WITHDRAWN: A prediction model for a concentrating solar thermoelectric generator using artificial neural networks and extreme learning machines

Artificial bee colony algorithm–optimized error minimized extreme learning machine and its application in short-term wind speed prediction

A Hybrid Method Based on Extreme Learning Machine and Wavelet Transform Denoising for Stock Prediction

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