Training RBF NN Using Sine-Cosine Algorithm for Sonar Target Classification

• Autorzy: Wang Yixuan , Yuan LiPing , Khishe Mohammad , Moridi Alaveh , Mohammadzade Fallah

Identyfikatory

DOI

10.24425/aoa.2020.135281

• Języki publikacji: EN

Abstrakty

EN

Radial basis function neural networks (RBF NNs) are one of the most useful tools in the classification of the sonar targets. Despite many abilities of RBF NNs, low accuracy in classification, entrapment in local minima, and slow convergence rate are disadvantages of these networks. In order to overcome these issues, the sine-cosine algorithm (SCA) has been used to train RBF NNs in this work. To evaluate the designed classifier, two benchmark underwater sonar classification problems were used. Also, an experimental underwater target classification was developed to practically evaluate the merits of the RBF-based classifier in dealing with high-dimensional real world problems. In order to have a comprehensive evaluation, the classifier is compared with the gradient descent (GD), gravitational search algorithm (GSA), genetic algorithm (GA), and Kalman filter (KF) algorithms in terms of entrapment in local minima, the accuracy of the classification, and the convergence rate. The results show that the proposed classifier provides a better performance than other compared classifiers as it classifies the sonar datasets 2.72% better than the best benchmark classifier, on average.

Słowa kluczowe

EN

classifiers; radial basis function neural network; sine-cosine algorithm; sonar

• Wydawca: Instytut Podstawowych Problemów Techniki PAN ; Komitet Akustyki PAN ; Polskie Towarzystwo Akustyczne
• Czasopismo: Archives of Acoustics
• Rocznik: 2020
• Tom: Vol. 45, No. 4
• Strony: 753--764
• Opis fizyczny: Bibliogr. 40 poz., rys., tab., wykr.

Twórcy

autor

Wang Yixuan

• School of Information Engineering, Wuhan University of Technology, China

autor

Yuan LiPing

• School of Information Engineering, Wuhan University of Technology, China
• School of Information Engineering, Wuhan Huaxia University of Technology, China

autor

Khishe Mohammad

• Department of Marine Electronic and Communication Engineering, Imam Khomeini Marine Science University of Nowshahr, Nowshahr, Iran

autor

Moridi Alaveh

• Department of Electrical Engineering, Iran University of Science and Technology, Narmak, Tehran, Iran

autor

Mohammadzade Fallah

• Department of Marine Electronic and Communication Engineering, Imam Khomeini Marine Science University of Nowshahr, Nowshahr, Iran

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).