An implementation of the Levenberg–Marquardt algorithm for simultaneous-energy-gradient fitting using two-layer feed-forward neural networks

Nguyen-Truong, Hieu T.; Le, Hung

doi:10.1016/j.cplett.2015.04.019

Cited by 47 publications

(27 citation statements)

References 25 publications

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“…The Levenberg-Marquardt (LM) training algorithm can be defined as a data driven computing method based on artificial intelligence (AI) concepts, which, more specifically, is able to correlate inversely and numerically, the nonlinear relationships between a set of individual variables (IVs) and outputs via their characteristic mathematical topology (Nguyen-Truong and Le, 2015;Ahmadi et al, 2016;Jaeel et al, 2016). The basic concept behind the LM method is to correlate the connections between IVs and model output, without assuming a prior formula defining this correlation (Sharma et al, 2017).…”

Section: The Lm Model Developmentmentioning

confidence: 99%

“…The cross-validation set is piloted to deliver an independent check of network performance, to avoid the model overfitting and to terminate the training process at a minimum MSE error (Nguyen-Truong and Le, 2015). The third cluster testing dataset was used to evaluate the models' ability to generalise and the validity of the optimum ANN model via using the last 15% of unseen data, after selecting the appropriate network weights and biases (Ahmadi et al, 2016;Shahin, 2016).…”

Section: The Lm Model Developmentmentioning

confidence: 99%

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Performance analysis of an evolutionary LM algorithm to model the load-settlement response of steel piles embedded in sandy soil

et al. 2019

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This study was implemented to examine pile load-settlement response and to develop a rapid, highly efficient predictive intelligent model, using a new computational intelligence (CI) algorithm. To achieve this aim, a series of experimental pile load tests were performed on steel, closed-ended pile models consisting of three piles with aspect ratios of 25, 17, and 12 in an attempt to make site in-situ pile-load tests unnecessary. An optimised, evolutionary, supervised Levenberg-Marquardt (LM) training algorithm was used for this process due to its remarkably robust performance. The model piles were penetrated and tested in three sand relative densities; dense, medium, and loose. Applied load (P), pile effective length (lc), pile flexural rigidity (EA), pile slenderness ratio (lc/d) and interface friction angle (δ) were identified, based on a comprehensive statistical analysis, as these parameters play a key role in governing pile settlement. To evaluate the efficiency and the generalisation ability of the proposed algorithm, graphical comparisons were made between the proposed algorithm and the experimental results with further comparisons made with conventional prediction approaches. The results revealed outstanding agreement between the targeted and predicted pile-load settlement with a coefficient of correlation of 0.985 and a Pearson's correlation coefficient, P = 2.22 x10-32 and root mean square error (RMSE) of 0.059 respectively. This, in parallel with a non-significant mean square error level (MSE) of 0.002, validates the feasibility of the proposed method and its potential in future applications.

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Section: The Lm Model Developmentmentioning

confidence: 99%

Section: The Lm Model Developmentmentioning

confidence: 99%

Performance analysis of an evolutionary LM algorithm to model the load-settlement response of steel piles embedded in sandy soil

et al. 2019

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“…The measuring accuracy indicators of the trained LM algorithm was firstly assessed during the learning or training process. The aforementioned training algorithm was used, as it is a most efficient and reliable method amongst all other feedforward artificial intelligence (AI) methods (Jeong and Kim, 2005;Nguyen-Truong and Le, 2015).…”

Section: The Lm Algorithm Measuring Performancementioning

confidence: 99%

Artificial neural network (ANN) approach for modelling of pile settlement of open-ended steel piles subjected to compression load

Jebur

Atherton

Khaddar

et al. 2018

European Journal of Environmental and Civil Engineering

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“…Therefore, PCA and the RBF neural network model were used in this paper to achieve accurate recognition of drinking-driving behaviors. To simplify the RBF neural network and avoid long training and local minima, the LM algorithm [12][13][14] was used to train the neural network, rather than the gradient descent method.…”

Section: Drinking-driving Recognition Model Based On Pca and Rbf Neurmentioning

confidence: 99%

Recognition Method of Drinking-driving Behaviors Based on PCA and RBF Neural Network

Sun

Zhang

Wang

et al. 2018

PROMET

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Drinking-driving behaviors are important causes of road traffic injuries, which are serious threats to the lives and property of traffic participants. Therefore, reducing the occurrences of drinking-driving behaviors has become an important problem of traffic safety research. Forty-eight male drivers and six female drivers who could drink moderate alcohol were chosen as participants. The drivers' physiological data, operation behavior data, car running data, and driving environment data were collected by designing various virtual traffic scenes and organizing drivers to conduct driving simulation experiments. The original variables were analyzed by the Principal Component Analysis (PCA), and seven principal components were extracted as the input vector of the Radial Basis Function (RBF) neural network. The principal component data was used to train and verify the RBF neural network. The Levenberg-Marquardt (LM) algorithm was chosen to train the parameters of the neural network and build a drinking-driving recognition model based on PCA and RBF neural network to realize an accurate recognition of drinking-driving behaviors. The test results showed that the drinking-driving recognition model based on PCA and RBF neural network could identify drinking drivers accurately during driving process with a recognition accuracy of 92.01%, and the operation efficiency of the model was high. The research can provide useful reference for prevention and treatment of drinking and driving and traffic safety maintenance. KEY WORDStraffic safety; drinking-driving behaviors; recognition method; principal component analysis; radial basis function neural network;

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An implementation of the Levenberg–Marquardt algorithm for simultaneous-energy-gradient fitting using two-layer feed-forward neural networks

Cited by 47 publications

References 25 publications

Performance analysis of an evolutionary LM algorithm to model the load-settlement response of steel piles embedded in sandy soil

Performance analysis of an evolutionary LM algorithm to model the load-settlement response of steel piles embedded in sandy soil

Artificial neural network (ANN) approach for modelling of pile settlement of open-ended steel piles subjected to compression load

Recognition Method of Drinking-driving Behaviors Based on PCA and RBF Neural Network

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