Training RBF NN Using Sine-Cosine Algorithm for Sonar Target Classification
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 overc...
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| Language: | English |
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Institute of Fundamental Technological Research Polish Academy of Sciences
2020-11-01
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| Series: | Archives of Acoustics |
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| Online Access: | https://acoustics.ippt.pan.pl/index.php/aa/article/view/2490 |
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| author | Yixuan WANG LiPing YUAN Mohammad KHISHE Alaveh MORIDI Fallah MOHAMMADZADE |
| author_facet | Yixuan WANG LiPing YUAN Mohammad KHISHE Alaveh MORIDI Fallah MOHAMMADZADE |
| author_sort | Yixuan WANG |
| collection | DOAJ |
| description | 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 RBFbased 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. |
| format | Article |
| id | doaj-art-364fa040a3ad4ad0ba335ea65e2beec6 |
| institution | DOAJ |
| issn | 0137-5075 2300-262X |
| language | English |
| publishDate | 2020-11-01 |
| publisher | Institute of Fundamental Technological Research Polish Academy of Sciences |
| record_format | Article |
| series | Archives of Acoustics |
| spelling | doaj-art-364fa040a3ad4ad0ba335ea65e2beec62025-08-20T02:52:46ZengInstitute of Fundamental Technological Research Polish Academy of SciencesArchives of Acoustics0137-50752300-262X2020-11-0145410.24425/aoa.2020.135281Training RBF NN Using Sine-Cosine Algorithm for Sonar Target ClassificationYixuan WANG0LiPing YUAN1Mohammad KHISHE2Alaveh MORIDI3Fallah MOHAMMADZADE4Wuhan University of Technology1) Wuhan University of Technology 2) Wuhan Huaxia University of TechnologyIran University Of Science and TechnologyIran University of Science and TechnologyImam Khomeini Marine Science University of NowshahrRadial 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 RBFbased 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.https://acoustics.ippt.pan.pl/index.php/aa/article/view/2490classifiersradial basis function neural networksine-cosine algorithmsonar |
| spellingShingle | Yixuan WANG LiPing YUAN Mohammad KHISHE Alaveh MORIDI Fallah MOHAMMADZADE Training RBF NN Using Sine-Cosine Algorithm for Sonar Target Classification Archives of Acoustics classifiers radial basis function neural network sine-cosine algorithm sonar |
| title | Training RBF NN Using Sine-Cosine Algorithm for Sonar Target Classification |
| title_full | Training RBF NN Using Sine-Cosine Algorithm for Sonar Target Classification |
| title_fullStr | Training RBF NN Using Sine-Cosine Algorithm for Sonar Target Classification |
| title_full_unstemmed | Training RBF NN Using Sine-Cosine Algorithm for Sonar Target Classification |
| title_short | Training RBF NN Using Sine-Cosine Algorithm for Sonar Target Classification |
| title_sort | training rbf nn using sine cosine algorithm for sonar target classification |
| topic | classifiers radial basis function neural network sine-cosine algorithm sonar |
| url | https://acoustics.ippt.pan.pl/index.php/aa/article/view/2490 |
| work_keys_str_mv | AT yixuanwang trainingrbfnnusingsinecosinealgorithmforsonartargetclassification AT lipingyuan trainingrbfnnusingsinecosinealgorithmforsonartargetclassification AT mohammadkhishe trainingrbfnnusingsinecosinealgorithmforsonartargetclassification AT alavehmoridi trainingrbfnnusingsinecosinealgorithmforsonartargetclassification AT fallahmohammadzade trainingrbfnnusingsinecosinealgorithmforsonartargetclassification |