Correction to: Efficient Hardware Architectures for 1D- and MD-LSTM Networks

Rybalkin, Vladimir; Sudarshan, Chirag; Weis, Christian; Lappas, Jan; Wehn, Norbert; Cheng, Li

doi:10.1007/s11265-021-01684-w

Cited by 2 publications

(2 citation statements)

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“…Two parameters, the hidden layer nodes and the learning rate, have important effects on the performance of LSTM [69] , [70] , [71] . Generally, the higher the hidden layer codes, the higher the computational accuracy and complexity of the LSTM.…”

Section: Optimization Of Lstm and Resultsmentioning

confidence: 99%

Strength prediction and application of cemented paste backfill based on machine learning and strength correction

Zhang

et al. 2022

Heliyon

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Section: Optimization Of Lstm and Resultsmentioning

confidence: 99%

Strength prediction and application of cemented paste backfill based on machine learning and strength correction

Zhang

et al. 2022

Heliyon

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“…Previous studies have shown that the number of hidden layer nodes and the learning rate have a significant effect on the performance of LSTM [46,[60][61][62]. The learning rate determines whether and when a neural network can converge.…”

Section: Optimization Of Lstmmentioning

confidence: 99%

Strength Investigation and Prediction of Superfine Tailings Cemented Paste Backfill Based on Experiments and Intelligent Methods

Zhang

et al. 2023

Materials

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The utilization of solid waste for filling mining presents substantial economic and environmental advantages, making it the primary focus of current filling mining technology development. To enhance the mechanical properties of superfine tailings cemented paste backfill (SCPB), this study conducted response surface methodology experiments to investigate the impact of various factors on the strength of SCPB, including the composite cementitious material, consisting of cement and slag powder, and the tailings’ grain size. Additionally, various microanalysis techniques were used to investigate the microstructure of SCPB and the development mechanisms of its hydration products. Furthermore, machine learning was utilized to predict the strength of SCPB under multi-factor effects. The findings reveal that the combined effect of slag powder dosage and slurry mass fraction has the most significant influence on strength, while the coupling effect of slurry mass fraction and underflow productivity has the lowest impact on strength. Moreover, SCPB with 20% slag powder has the highest amount of hydration products and the most complete structure. When compared to other commonly used prediction models, the long-short term memory neural network (LSTM) constructed in this study had the highest prediction accuracy for SCPB strength under multi-factor conditions, with root mean square error (RMSE), correlation coefficient (R), and variance account for (VAF) reaching 0.1396, 0.9131, and 81.8747, respectively. By optimizing the LSTM using the sparrow search algorithm (SSA), the RMSE, R, and VAF improved by 88.6%, 9.4%, and 21.9%, respectively. The research results can provide guidance for the efficient filling of superfine tailings.

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Correction to: Efficient Hardware Architectures for 1D- and MD-LSTM Networks

Cited by 2 publications

References 0 publications

Strength prediction and application of cemented paste backfill based on machine learning and strength correction

Strength prediction and application of cemented paste backfill based on machine learning and strength correction

Strength Investigation and Prediction of Superfine Tailings Cemented Paste Backfill Based on Experiments and Intelligent Methods

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