Prediction Model of Suspension Density in the Dense Medium Separation System Based on LSTM

Cheng, Zheng; Deng, Jianjun; Hong, Zhixin; Wang, Guanghui

doi:10.3390/pr8080976

Cited by 10 publications

(4 citation statements)

References 25 publications

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“…If the number of nodes is too large, it is prone to over-fitting, which reduces the generalization ability of the model; if the number of nodes is too small, the model cannot fully learn the characteristics of time series data and reduces the performance of the model. To solve the above problems, scholars have proposed a variety of discrimination methods to determine the number of hidden layer units based on many experimental studies (Liu et al, 2012;Han et al, 2020;Zheng et al, 2020). In this study, the empirical formula is used to determine the value range of the number of hidden layer units:…”

Section: Model Parameter Analysismentioning

confidence: 99%

Prediction Method of TBM Tunneling Parameters Based on PSO-Bi-LSTM Model

Zhang

Zhu

et al. 2022

Front. Earth Sci.

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With the wide application of full-face rock tunnel boring machine (TBM) in tunnel construction, the self-adaptive adjustment of TBM tunneling parameters is of great significance for the safety and efficiency of TBM tunnelling. Aiming at the shortcomings of the current TBM data mining capability and optimization methods of tunneling parameters, this paper proposes a prediction method of TBM tunneling parameters based on particle swarm optimization-bi-directional long short-term memory (PSO-Bi-LSTM) model, which selects the complete tunneling cycle data to predict the TBM tunneling parameters, and uses a number of numerical methods such as binary state discriminant function and 3σ criterion to preprocess the operation data of TBM3 bid section of Songhua River water conveyance project. By comparing with the Bi-LSTM model and evaluating the prediction effect under different surrounding rock levels, the applicability and prediction performance of the model to different strata are verified. The results show that the prediction accuracy of the model is proportional to the surrounding rock grade. Compared with the Bi-LSTM, the overall prediction effect of the proposed PSO-Bi-LSTM model is better, which can assist the intelligent construction of TBM with similar geological conditions.

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Section: Model Parameter Analysismentioning

confidence: 99%

Prediction Method of TBM Tunneling Parameters Based on PSO-Bi-LSTM Model

Zhang

Zhu

et al. 2022

Front. Earth Sci.

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“…LSTM is a special kind of RNN that mainly solves the gradient disappearance and gradient explosion problems while training long sequences [19][20][21][22]. Compared with normal RNN, LSTM can have better performance in longer sequences.…”

Section: Lstm Algorithmmentioning

confidence: 99%

Research on Gas Concentration Prediction Based on the ARIMA-LSTM Combination Model

Fang

Yan

et al. 2023

Processes

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The current single gas prediction model is not sufficient for identifying and processing all the characteristics of mine gas concentration time series data. This paper proposes an ARIMA-LSTM combined forecasting model based on the autoregressive integrated moving average (ARIMA) model and the long short-term memory (LSTM) recurrent neural network. In the ARIMA-LSTM model, the ARIMA model is used to process the historical data of gas time series and obtain the corresponding linear prediction results and residual series. The LSTM model is used in further analysis of the residual series, predicting the nonlinear factors in the residual series. The prediction results of the combined model are compared separately with those of the two single models. Finally, RMSE, MAPE and R2 are used to evaluate the prediction accuracy of the three models. The results of the study show that the metrics of the combined ARIMA-LSTM model are R2 = 0.9825, MAPE = 0.0124 and RMSE = 0.083. The combined model has the highest prediction accuracy and the lowest error and is more suitable for the predictive analysis of gas data. By comparing the prediction results of a single model and the combined model on gas time series data, the applicability, validity and scientificity of the combined model proposed in this paper are verified, which is of great importance to accurate prediction and early warning of underground gas danger in coal mines.

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“…LSTM is a special type of recurrent neural network (RNN) that performs very well with long sequences of data, mainly solving gradient disappearance, gradient explosion, and overfitting problems when training long sequences [40][41][42][43]. RNN is an artificial neural network that operates on time-series data and can use back-propagation algorithms to learn and adapt to the relationship between inputs and outputs.…”

Section: Lstm Modelmentioning

confidence: 99%

Analysis of the Composition of Ancient Glass and Its Identification Based on the Daen-LR, ARIMA-LSTM and MLR Combined Process

Wang

et al. 2023

Applied Sciences

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The glass relics are precious material evidence of the early trade and cultural exchange between the East and the West. To explore the cultural differences and trade development between early China and foreign countries, it is extremely important to classify glass cultural relics. Despite their similar appearances, Chinese glass contains more lead, while foreign glass contains more potassium. In view of this, this paper proposes a joint Daen-LR, ARIMA-LSTM, and MLR machine learning algorithm (JMLA) for the analysis and identification of the chemical composition of ancient glass. We separate the sampling points of ancient glass into two systems: lead-barium glass and high-potassium glass. Firstly, an improved logistic regression model based on a double adaptive elastic network (Daen-LR) is used to select variables with both Oracle and adaptive classification characteristics. Secondly, the ARIMA-LSTM model was used to establish the correlation curve of chemical composition before and after weathering and to predict the change in chemical composition with weathering. Thirdly, combining the data processed by the above two methods, a multiple linear regression model (MLR) is used to classify unknown glass products. It was shown that the sample obtained by this processing method has a very good fit. In comparison with other similar types of models like Decision Trees (DT), Random Forests (RF), Support Vector Machines (SVM), and Random Forests based on classification and regression trees (CART-RF), the classification accuracy of JMLA is 97.9% on the train set. The accuracy rate on the test set reached 97.6%. The results of the research demonstrate that JMLA can improve the accuracy of the glass type classification problem, greatly enhance the research efficiency of archaeological staff, and gain a more reliable result.

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Prediction Model of Suspension Density in the Dense Medium Separation System Based on LSTM

Cited by 10 publications

References 25 publications

Prediction Method of TBM Tunneling Parameters Based on PSO-Bi-LSTM Model

Prediction Method of TBM Tunneling Parameters Based on PSO-Bi-LSTM Model

Research on Gas Concentration Prediction Based on the ARIMA-LSTM Combination Model

Analysis of the Composition of Ancient Glass and Its Identification Based on the Daen-LR, ARIMA-LSTM and MLR Combined Process

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