The assessment of Levenberg–Marquardt and Bayesian Framework training algorithm for prediction of concrete shrinkage by the artificial neural network

Garoosiha, Hosein; Ahmadi, Jamal; Bayat, Hossein

doi:10.1080/23311916.2019.1609179

Cited by 31 publications

(11 citation statements)

References 10 publications

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“…However, a much better prediction performance can be obtained from the optimized LM-ANN (17 neurons) and BR-ANN (11 neurons) with an MSE and R 2 for the testing data set of 0.0014 and 0.9817 (LM-ANN) and 0.0164 and 0.9724 (BR-ANN), respectively. Since the LM algorithm is much simpler than the BR algorithm, 58,59 the optimized number of hidden neurons of LM-ANN (17 neurons) was found to be higher than that of the BR-ANN model (11 neurons). Table 1 summarizes MSE and R 2 as well as the number of hidden neurons of the three optimized ANN models.…”

Section: Resultsmentioning

confidence: 93%

Applied Artificial Neural Network for Hydrogen Sulfide Solubility in Natural Gas Purification

et al. 2021

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Solubility of hydrogen sulfide (H2S) in 46 single and blended physical absorbents, amines, ionic liquids, and hybrid absorbents of amines + ionic liquids and amines + physical absorbents was successfully predicted based on artificial neural networks (ANNs). Three neural network algorithms of Levenberg–Marquardt (LM), Bayesian regularization (BR), and scaled conjugate gradient (SCG) were applied for architecting the ANN models. The results showed that both the number of hidden neurons and the prediction algorithm affected the prediction of H2S solubility. Based on the mean square error (MSE) and determination coefficient (R 2), the most attractive model was the LM-ANN model with 17 hidden neurons. As a result, very satisfactory prediction performance (for the testing data set) with an MSE of 0.0014 and an R 2 of 0.9817 was obtained from the developed LM-ANN model. Additionally, a parity chart confirmed that the predicted solubility of H2S well aligned with the experimental data. To effectively absorb H2S and maintain high solubility of H2S, the absorbent should be well complied with the operating pressure. For a low-pressure range of less than 100 kPa, amines are very attractive. As the pressure elevated to 100–1000 kPa, amines and hybrid amine + physical absorbents are suggested. Lastly, at a high pressure over 1000 kPa, physical absorbents and ionic liquids are recommended.

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Section: Resultsmentioning

confidence: 93%

Applied Artificial Neural Network for Hydrogen Sulfide Solubility in Natural Gas Purification

et al. 2021

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“…A deeper analysis of the scientific publications revealed positive results from the adoption of certain artificial neural network (ANN) training algorithms to solve prediction problems via time-series forecasting, especially, a modified Levenberg-Marquardt (LM) algorithm by Garoosiha et al [42]. Two authors developed this algorithm independently: Levenberg [43] and Marquardt [44].…”

Section: Justification Of the Selected Prediction Methodsmentioning

confidence: 99%

Application of Neural Network Predictive Control Methods to Solve the Shipping Container Sway Control Problem in Quay Cranes

2021

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Smart control systems are mostly applied in industry to control the movements of heavy machinery while optimizing overall operational efficiency. Major shipping companies use large quay cranes to load and unload containers from ships and still rely on the experience of on-site operators to perform transportation control procedures using joysticks and visual contact methods. This paper presents the research results of an EU-funded project for the Klaipeda container terminal to develop a novel container transportation security and cargo safety assurance method and system. It was concluded that many risks arise during the container handling procedures performed by the quay cranes and operators. To minimize these risks, the authors proposed controlling the sway of the spreader using a model predictive control method which applies a multi-layer perceptron (MLP) neural network (NN). The paper analyzes current neural network architectures and case studies and provides the engineering community with a unique case study which applies real operation statistical data. Several key training algorithms were tested, and the initial results suggest that the Levenberg-Marquardt (LM) algorithm and variable learning rate backpropagation perform better than methods which use the multi-layer perceptron neural network structure.

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“…In the literature, several works have shown that the Levenberg-Marquardt algorithm presents good converge characteristics and generates accurate models. [61][62][63][64][65][66][67] In addition, the initial guess of weights is random according to the Nguyen-Widrow algorithm. 68 The early stopping method 52 is here considered as a regularization technique.…”

Section: Multilayer Perceptronmentioning

confidence: 99%

Inverse analysis of hydraulic fracturing tests based on artificial intelligence techniques

Abreu

Mejia

Roehl

2022

Num Anal Meth Geomechanics

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In the last decades, the study of fluid flow through discontinuities such as fractures has received significant attention from the petroleum industry owing to the discovery of naturally fractured reservoirs. Predicting the behavior of this type of reservoir is challenging and includes, as a crucial step, determining its geomechanical parameters. Several in situ tests and empirical relationships have been established to estimate these parameters. Nevertheless, these procedures have important limitations. This work proposes an artificial intelligence-based methodology to identify geomechanical parameters from borehole injection pressure curves obtained during hydraulic fracturing tests. This methodology applies a genetic algorithm to minimize the sum of squared differences between the observed and predicted borehole pressure curves. Furthermore, an artificial neural network is adopted to build a proxy model that substitutes hydraulic fracturing numerical simulations, substantially reducing computational time. A proper recursive strategy is adopted to predict the borehole pressure curves.Additionally, a hyperparameter tuning technique selects appropriate neural network hyperparameters based on the multistep-ahead prediction error. Finally, the framework is applied in a KGD problem that models hydraulic fracture propagation. The benchmark confirms the capability of the proposed methodology to identify geomechanical parameters from fracturing tests.

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The assessment of Levenberg–Marquardt and Bayesian Framework training algorithm for prediction of concrete shrinkage by the artificial neural network

Cited by 31 publications

References 10 publications

Applied Artificial Neural Network for Hydrogen Sulfide Solubility in Natural Gas Purification

Applied Artificial Neural Network for Hydrogen Sulfide Solubility in Natural Gas Purification

Application of Neural Network Predictive Control Methods to Solve the Shipping Container Sway Control Problem in Quay Cranes

Inverse analysis of hydraulic fracturing tests based on artificial intelligence techniques

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