Optimized neural network to predict the experimental minimum period of coal spontaneous combustion

Xiao, Yang; Cao, Yuyan; Zhong, Kai-Qi; Yin, Liang

doi:10.1007/s11356-021-18387-1

Cited by 7 publications

(2 citation statements)

References 25 publications

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“…(1) The establishment process of PSO‐ANN model is as follows, 42 as shown in Figure 4. ① Determine the neural network structure; ② Initialize the weight and threshold of the ANN; ③ Establish the relationship between PSO and weight and threshold, and initialize the PSO; ④ Calculate the particle fitness value and update the position and speed of each particle; ⑤ Calculate the error of PSO‐ANN.…”

Section: Principles Of the Gef And Machine Learning‐based Modelsmentioning

confidence: 99%

“…(2) The establishment process of GO‐ANN structure is as follows, 42 as shown in Figure 5. ① Establishment of ANN structure; ② Train the data using ANN and generate the initial population; ③ Initial coding and pre‐processing of GO algorithm; ④ Use different initial values for fitness calculation; ⑤ Genetic manipulation processing; ⑥ Obtain the optimal weight and threshold value (or repeat ④ and ⑤, and finally obtain the optimal values); ⑦ Assign values to neural network, find the global optimal solution and establish the GO‐ANN structure model.…”

Section: Principles Of the Gef And Machine Learning‐based Modelsmentioning

confidence: 99%

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Prediction of the stability of gob‐side entry formation by roof cutting by machine learning‐based models

Gao

Gai

et al. 2023

Energy Science & Engineering

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Gob-side entry formation by roof cutting is a new technology for no pillar coal mining, which can maximize coal resources and reduce roadway drivage ratio.However, the mechanical behavior of the formed entry is complex while it is crucial to ensure the stability of the entry for mining safety. This paper proposed a machine learning-based method for predicting the stability of the formed entry, which combined artificial neural network (ANN) with particle swarm optimization (PSO) algorithm or genetic optimization (GO) algorithm.The data set from 75 coal mining faces from 2009 to 2022 was employed to train and test the models. A descriptive variable of dynamic unstable distance was introduced to evaluate the stability state of the formed entry and six other parameters were chosen as influence parameters. The two intelligent models were compared with each other to have a comprehensive assessment. Model assessment indices such as R 2 , mean absolute error, mean absolute percentage error, and root mean square error were used to evaluate the accuracy of the models. The results of both developed models are promising, and the predictive accuracy of the PSO-ANN model is higher than that of the GO-ANN model. Through sensitivity analyses, it has been found that the coal seam thickness and roof rock hardness are the most important parameters for influencing entry stability. The developed method provides a practical tool for the prediction of entry stability and the optimization of entry design.

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Section: Principles Of the Gef And Machine Learning‐based Modelsmentioning

confidence: 99%

Section: Principles Of the Gef And Machine Learning‐based Modelsmentioning

confidence: 99%