Application of Improved Variable Learning Rate Back Propagation Neural Network in Energy Dispersion X-Ray Fluorescence Quantitative Analysis

Li, Fēi; Ge, Liangquan; Dan, Wenxuan; Gu, Yuantao; He, Qingju; Sun, Ke

doi:10.1109/icccbda.2019.8725682

Cited by 5 publications

(5 citation statements)

References 7 publications

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“…Least-squares fitting methods are parameterized mathematical models for finding a set of data points by minimizing an objective (variable) expressed as the sum of the squares of the errors between the model function and a set of data points [36]. The following methods are used for the 1D neural network training: Broyden-Fletcher-Goldfarb-Shanno (Quasi-Newton) [37], Levenberg-Marquardt [36], Resilient Backpropagation [38], and Variable Learning Rate Backpropagation [39,40]. this paper we assume the data proceed from a single DRU.…”

Section: Inference Of Spectrum Footprints By Machine Learningmentioning

confidence: 99%

White spaces pattern finding and inference based on machine learning for multi-frequency spectrum footprints

et al. 2023

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Section: Inference Of Spectrum Footprints By Machine Learningmentioning

confidence: 99%

White spaces pattern finding and inference based on machine learning for multi-frequency spectrum footprints

et al. 2023

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“…Bhati et al [5] showed that single layer neural network can give 100% recognition accuracy if correct learning rate is assigned to it. Li et al [23] showed that the improved VLBP can quickly and accurately predict the concentration of target elements in Energy Dispersive X-Ray Fluorescence (EDXRF). In this paper, we show that GD takes more epochs.…”

Section: Variable Learning Rate Backpropagation (Gd)mentioning

confidence: 99%

Effect of Hidden Neuron Size on Different Training Algorithm in Neural Network

Kumar¹,

Sodhi²

2022

Comm. Math. Appl.

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We use different types of training algorithms in the neural network. But, we cannot say which kind of training algorithm is fast for a given problem. So, in this survey paper, we are trying to find which types of training algorithm are better for categorization problems. For this purpose, we used ten types of training algorithms in the pattern network in MATLAB. We used Levenberg-Marquardt (LM), Bayesian regularization backpropagation (BR), BFGS Quasi-Newton (BFG), Resilient Backpropagation (RP), Scaled Conjugate gradient backpropagation (SCG), Conjugate Gradient with Powell/Beale Restarts (CGB), Fletcher-Powell Conjugate Gradient (CGF), Polak-Ribiere Conjugate Gradient (GDM), One Step Secant (OSS), and Variable Learning Rate Backpropagation (GD) algorithm. In this survey paper, we also check, affects of these algorithms on neural network when we applied different types of hidden neuron size. During this survey we found some new facts. We found that RP, SCG, CGB, CGF and OSS are fastest algorithms. BFG takes more time with respect to hidden neuron size. GDM and GD take more epochs. BR algorithm is not acceptable for image categorization.

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“…This is based on finding the partial derivative of the error function to update the weights of the connections between neurons. The training process based on the gradient descent often gets trapped in local minima or premature convergence and sometimes causes over-fitting and under-fitting problems, especially in the case of the presence of a multilayer neural network (38)(39)(40). The multilayer neural network is normally linked with a large search space, multi-local minima points, non-differential functions, and complex multi-dimensional curves (41)(42)(43)(44).…”

Section: Literature Reviewmentioning

confidence: 99%

Hybrid Elman Neural Network and an Invasive Weed Optimization Method for Bridge Defect Recognition

Abdelkader

Moselhi

Marzouk

et al. 2020

Transportation Research Record: Journal of the Transportation R

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Existing bridges are aging and deteriorating, raising concerns for public safety and the preservation of these valuable assets. Furthermore, the transportation networks that manage many bridges face budgetary constraints. This state of affairs necessitates the development of a computer vision-based method to alleviate shortcomings in visual inspection-based methods. In this context, the present study proposes a three-tier method for the automated detection and recognition of bridge defects. In the first tier, singular value decomposition ([Formula: see text]) is adopted to formulate the feature vector set through mapping the most dominant spatial domain features in images. The second tier encompasses a hybridization of the Elman neural network ([Formula: see text]) and the invasive weed optimization (I[Formula: see text]) algorithm to enhance the prediction performance of the ENN. This is accomplished by designing a variable optimization mechanism that aims at searching for the optimum exploration–exploitation trade-off in the neural network. The third tier involves validation through comparisons against a set of conventional machine-learning and deep-learning models capitalizing on performance prediction and statistical significance tests. A computerized platform was programmed in C#.net to facilitate implementation by the users. It was found that the method developed outperformed other prediction models achieving overall accuracy, F-measure, Kappa coefficient, balanced accuracy, Matthews’s correlation coefficient, and area under curve of 0.955, 0.955, 0.914, 0.965, 0.937, and 0.904, respectively as per cross validation. It is expected that the method developed can improve the decision-making process in bridge management systems.

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Application of Improved Variable Learning Rate Back Propagation Neural Network in Energy Dispersion X-Ray Fluorescence Quantitative Analysis

Cited by 5 publications

References 7 publications

White spaces pattern finding and inference based on machine learning for multi-frequency spectrum footprints

White spaces pattern finding and inference based on machine learning for multi-frequency spectrum footprints

Effect of Hidden Neuron Size on Different Training Algorithm in Neural Network

Hybrid Elman Neural Network and an Invasive Weed Optimization Method for Bridge Defect Recognition

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