Radial Basis Gated Unit-Recurrent Neural Network (RBGU-RNN) Algorithm

Abstract Radial Basis Gated Unit-Recurrent Neural Network (RBGU-RNN) algorithm is a new architecture-based on recurrent neural network which combines a Radial Basis Gated Unit within the Long Short Term Memory (LSTM) network architecture. This unit then gives an advantage to RBGU-RNN over the existi...
Ausführliche Beschreibung

Abstract Radial Basis Gated Unit-Recurrent Neural Network (RBGU-RNN) algorithm is a new architecture-based on recurrent neural network which combines a Radial Basis Gated Unit within the Long Short Term Memory (LSTM) network architecture. This unit then gives an advantage to RBGU-RNN over the existing LSTM network. Firstly, given that the RBGU is just an activation unit and which do not perform any weighted operations as it should in a classical neuron unit, it has an advantage for not propagating (duplicating) error as compared to the LSTM. Secondly, due to the fact that this unit is located at the beginning of the network treatment workflow, it provides standardization to the data set, before they are run into the weighted units, which is not the case of a simple LSTM. This study then provided a theoretical and experimental comparison of the LSTM and RBGU-RNN. Indeed, using a real world call data record, precisely a survey on the end user cell network data traffic, we built up a cellular traffic prediction model. We start with ARIMA model which permit us to choose the number of time steps needed to build the RBGU-RNN prediction model that is the number of time steps needed to predict the next individual in the time series. The results show that RBGU-RNN accurately predict cellular data traffic with great success in generalization than LSTM. The R-squared statistics or determination coefficients show that %$58.31 \%%$ of user traffic consumption can be explained by LSTM model, while %$96.86 \%%$ of the user traffic consumption can be done by RBGU-RNN model in the training set. Likewise, in the test set, we found that %$61.24 \%%$ of user traffic consumption can also be explained by LSTM model and %$95.20 \%%$ can be done by RBGU-RNN. Also, the RBGU-RNN has more efficient gradient descent than the standard LSTM by analysing and experimenting the graphs given by the Mean Squared Error (MSE), the Mean Absolute Percentage Error (MAPE) and the Maximum Absolute Error (MAXAE) functions over the number of iteration. Ausführliche Beschreibung

Autor*in:	Rollin, Ndom Francis [verfasserIn] Giquel, Sassa Chantal, Mveh-Abia Raoul, Ayissi Remy, Etoua Yves, Emvudu

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Neural networks Feed forward algorithm Recurrent neural networks Gated recurrent unit Long short term memory

Anmerkung:

© The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Übergeordnetes Werk:	Enthalten in: SN Computer Science - Singapore : Springer Singapore, 2020, 5(2023), 1 vom: 06. Dez.
Übergeordnetes Werk:	volume:5 ; year:2023 ; number:1 ; day:06 ; month:12

Links:	Volltext

DOI / URN:	10.1007/s42979-023-02376-x

Katalog-ID:	SPR05400585X