A Global-best Harmony Search based Gradient Descent Learning FLANN (GbHS-GDL-FLANN) for data classification

Naik, Bighnaraj; Nayak, Janmenjoy; Behera, Himansu Sekhar

doi:10.1016/j.eij.2015.09.001

Cited by 9 publications

(1 citation statement)

References 227 publications

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“…The quasi-Newton BFGS is an iterative algorithm that belongs to the quasi-Newton methods, which avoid the inversion of the Hessian matrix by directly calculating the inverse of a pseudo-Hessian matrix, improving the computational efficiency of the whole calculation [ 21 ] and demonstrating a better performance in the development of the ANN model architecture [ 22 ]. The batch gradient descent algorithm is based on calculating the coefficients in the direction of the negative gradient and is characterized by reducing the error of the neural network as rapidly as possible [ 23 ]. All the calculations necessary to develop the ANN model and the application of the optimization algorithms were carried out with MATLAB mathematical software.…”

Section: Methodsmentioning

confidence: 99%

Comparison of an artificial neural network and Gompertz model for predicting the dynamics of deaths from COVID-19 in México

2021

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The present work is focused on modeling and predicting the cumulative number of deaths from COVID-19 in México by comparing an artificial neural network (ANN) with a Gompertz model applying multiple optimization algorithms for the estimation of coefficients and parameters, respectively. For the modeling process, the data published by the daily technical report COVID-19 in Mexico from March 19th to September 30th were used. The data published in the month of October were included to carry out the prediction. The results show a satisfactory comparison between the real data and those obtained by both models with a R 2 > 0.999. The Levenberg–Marquardt and BFGS quasi-Newton optimization algorithm were favorable for fitting the coefficients during learning in the ANN model due to their fast and precision, respectively. On the other hand, the Nelder–Mead simplex algorithm fitted the parameters of the Gompertz model faster by minimizing the sum of squares. Therefore, the ANN model better fits the real data using ten coefficients. However, the Gompertz model using three parameters converges in less computational time. In the prediction, the inverse ANN model was solved by a genetic algorithm obtaining the best precision with a maximum error of 2.22% per day, as opposed to the 5.48% of the Gompertz model with respect to the real data reported from November 1st to 15th. Finally, according to the coefficients and parameters obtained from both models with recent data, a total of 109,724 cumulative deaths for the inverse ANN model and 100,482 cumulative deaths for the Gompertz model were predicted for the end of 2020.

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Section: Methodsmentioning

confidence: 99%