RETRACTED ARTICLE: A hybrid algorithm based on artificial bat and backpropagation algorithms for multiplicative neuron model artificial neural networks

Baş, Eren; Eğrioğlu, Erol; Yolcu, Ufuk

doi:10.1007/s12652-020-01950-y

Cited by 6 publications

(2 citation statements)

References 32 publications

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“…Metode backpropagation merupakan metode berbasis gradien yang banyak digunakan untuk perlatihan jaringan saraf feedforward (umpan maju) [17][18][19]. Perkembangan metode backpropagation dengan beberapa fungsi, diantaranya fungsi transfer dan perlatihan yang masing-masing fungsi juga dengan banyak teknik dan metode yang dapat digunakan untuk menyelesaikan penyelesaian masalah dengan tingkat kekompleksan yang tinggi.…”

Section: Pendahuluanunclassified

Algoritma Fungsi Perlatihan pada Machine Learning berbasis ANN untuk Peramalan Fenomena Bencana

Wanto

Defit

Windarto

2021

RESTI

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Research has been carried out with several training functions using standard backpropagation methods, One-Step Secant (OSS), and Bayesian regulation. The purpose of this study was to (i) analyze the Performance accuracy (Performance) of the standard backpropagation method and (ii) optimize the training function with the One-Step Secant (OSS) and Bayesian regulation methods to obtain comparison results of the three methods in the search for the best results implementation of disaster phenomenon forecasting data. The research method is based on quantitative methods with times-series data on disaster phenomena in Indonesia over the last ten years (2011-2020) which were analyzed using two network architecture models, namely 4-8-1 and 4-10-1. The results showed that the 4-8-1 architectural model with the Bayesian regulation training function method was able to optimize quite well through accelerating training time and resulted in a low MSE measurement, although not the lowest with an epoch value of 197 iterations and a Performance of 0.0148480766. The lowest epoch value is generated by the OSS method, but it Performs poorly. The best Performance is produced by the standard backpropagation method with the traingd training function, but the training process for achieving convergence is also too long. In general, it can be concluded that the 4-8-1 architectural model with Bayesian regulation can be used to predict (predict) the phenomenon of natural disasters in Indonesia because the training time to achieve convergence is not too long and Performs exceptionally well.

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Section: Pendahuluanunclassified

Algoritma Fungsi Perlatihan pada Machine Learning berbasis ANN untuk Peramalan Fenomena Bencana

Wanto

Defit

Windarto

2021

RESTI

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“…Back-propagation is widely used for training feedforward neural networks which are gradient-based algorithms [6][7][8]. Based on its development, Back-propagation has different functions, including the activation function (transfer) and the training function, each of which has many methods and techniques that can be used to solve complex computational problems.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Performance Traditional Back-propagation with Cyclical Rule for Forecasting Model

Wanto,

Ginantra,

Hendraputra

et al. 2022

matrik

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The traditional Back-propagation algorithm has several weaknesses, including long training times and significant iterations to achieve convergence. This study aims to optimize traditional Back-propagation using the cyclical rule method to cover these weaknesses. Optimization is done by changing the training function and standard Back-propagation parameters using the training function and cyclical rule parameters. After that, a comparison of the two results will be carried out. This study uses quantitative method of time-series data on coronavirus cases sourced from the Worldometer website, then analyzed using three forecasting models with five input layers, one hidden layer (5, 10, and 15 neurons) and one output layer. The results showed that the 5-10-1 model with the training function and cyclical rule parameters and the tansig and purelin activation functions could perform well in optimization, including faster training time and smaller iterations (epochs), MSE training performance, and better tests. Low and high accuracy (92%) with an error rate of 0.01. So it was concluded that the training function and cyclical rule parameters with the tansig and purelin activation functions were able to optimize the traditional Back-propagation method, and the 5-10-1 model could be used for forecasting active cases of the coronavirus in Asia

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A new single multiplicative neuron model artificial neural network based on black hole optimization algorithm: forecasting the amounts of clean water given to metropolis

Işık,

Bas,

Egrioglu

et al. 2024

Stoch Environ Res Risk Assess

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RETRACTED ARTICLE: A hybrid algorithm based on artificial bat and backpropagation algorithms for multiplicative neuron model artificial neural networks

Cited by 6 publications

References 32 publications

Algoritma Fungsi Perlatihan pada Machine Learning berbasis ANN untuk Peramalan Fenomena Bencana

Algoritma Fungsi Perlatihan pada Machine Learning berbasis ANN untuk Peramalan Fenomena Bencana

Optimization of Performance Traditional Back-propagation with Cyclical Rule for Forecasting Model

A new single multiplicative neuron model artificial neural network based on black hole optimization algorithm: forecasting the amounts of clean water given to metropolis

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