| DOI | Trouver le DOI : https://doi.org/10.1109/ICC45041.2023.10278933 |
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| Auteur | Rechercher : Sadr, Mohammad Amin Maleki; Rechercher : Zhu, Yeying; Rechercher : Hu, Peng1 |
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| Affiliation | - Conseil national de recherches Canada. Technologies numériques
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| Format | Texte, Article |
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| Conférence | ICC 2023 - IEEE International Conference on Communications, 28 May - 1 June, 2023, Rome, Italy |
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| Sujet | anomaly detection; genetic algorithm; Neural Networks; LSTM; RNN; GRU |
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| Résumé | In this paper, we use a variance-based genetic ensemble (VGE) of Neural Networks (NNs) to detect anomalies in the satellite's historical data. We use an efficient ensemble of the predictions from multiple Recurrent Neural Networks (RNNs) by leveraging each model's uncertainty level (variance). For prediction, each RNN is guided by a Genetic Algorithm (GA) which constructs the optimal structure for each RNN model. However, finding the model uncertainty level is challenging in many cases. Although the Bayesian NNs (BNNs)-based methods are popular for providing the confidence bound of the models, they cannot be employed in complex NN structures as they are computationally intractable. This paper uses the Monte Carlo (MC) dropout as an approximation version of BNNs. Then these uncertainty levels and each predictive model suggested by GA are used to generate a new model, which is then used for forecasting the TS and AD. Simulation results show that the forecasting and AD capability of the ensemble model outperforms existing approaches. |
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| Date de publication | 2023-10-23 |
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| Maison d’édition | IEEE |
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| Dans | |
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| Autre version | |
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| Langue | anglais |
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| Publications évaluées par des pairs | Oui |
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| Exporter la notice | Exporter en format RIS |
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| Signaler une correction | Signaler une correction (s'ouvre dans un nouvel onglet) |
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| Identificateur de l’enregistrement | 78d1f938-28ad-4344-8b2b-c061fb955162 |
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| Enregistrement créé | 2023-11-03 |
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| Enregistrement modifié | 2023-11-03 |
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