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Wireless Personal Communications
Erschienen in: Wireless Personal Communications 4/2019

10.05.2019

Classification of Sonar Targets Using an MLP Neural Network Trained by Dragonfly Algorithm

verfasst von: Mohammad Khishe, Abbas Safari

Erschienen in: Wireless Personal Communications | Ausgabe 4/2019

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Abstract

Due to the compatibility of the designed classifiers with MLP Neural Networks (MLP NNs), in this article, MLP NNs have been used to identify and classify active and passive sonar targets. On the one hand, the great importance of precise and immediate classification of sonar targets, and on the other hand, being trapped in local minimums and the low convergence speed in classic MLP NNs have led the newly proposed Dragonfly Algorithm (DA) to be offered for training MLP NNs. In order to assess the performance of the designed classifier, this algorithm have been compared with BBO, GWO, ALO, ACO, GSA and MVO algorithms in terms of precision of classification, convergence speed and the ability to avoid local optimum. To have a comprehensive comparison, the three sets of active and passive data were used. Simulation results indicate that DA-based classification have better results in all three datasets compared to benchmark algorithms.
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Metadaten
Titel
Classification of Sonar Targets Using an MLP Neural Network Trained by Dragonfly Algorithm
verfasst von
Mohammad Khishe
Abbas Safari
Publikationsdatum
10.05.2019
Verlag
Springer US
Erschienen in
Wireless Personal Communications / Ausgabe 4/2019
Print ISSN: 0929-6212
Elektronische ISSN: 1572-834X
DOI
https://doi.org/10.1007/s11277-019-06520-w

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