Archives of Acoustics, 45, 4, pp. 753–764, 2020
10.24425/aoa.2020.135281

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

Yixuan WANG
Wuhan University of Technology
China

LiPing YUAN
1) Wuhan University of Technology 2) Wuhan Huaxia University of Technology
China

Mohammad KHISHE
Iran University Of Science and Technology
Iran, Islamic Republic of

Alaveh MORIDI
Iran University of Science and Technology
Iran, Islamic Republic of

Fallah MOHAMMADZADE
Imam Khomeini Marine Science University of Nowshahr
Iran, Islamic Republic of

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.
Keywords: classifiers; radial basis function neural network; sine-cosine algorithm; sonar
Full Text: PDF
Copyright © The Author(s). This is an open-access article distributed under the terms of the Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0).

References

Abedifar V., Eshghi M., Mirjalili S., Mirjalili S.M. (2013), An optimized virtual network mapping using PSO in cloud computing, 21st Iranian Conference on Electrical Engineering, pp. 1–6, doi: 10.1109/IranianCEE.2013.6599723.

Abu-Mouti F.S., El-Hawary M.E. (2012), Overview of Artificial Bee Colony (ABC) algorithm and its applications, 2012 IEEE International Systems Conference SysCon, pp. 1–6, doi: 10.1109/SysCon.2012.6189539.

Aljarah I., Faris H., Mirjalili S., Al-Madi N. (2016), Training radial basis function networks using biogeography-based optimizer, Neural Computing and Applications, 29(7): 529–553, doi: 10.1007/s00521-016-2559-2.

Auer P., Burgsteiner H., Maass W. (2008), A learning rule for very simple universal approximators consisting of a single layer of perceptrons, Neural Networks, 21(5): 786–795, doi: 10.1016/j.neunet.2007.12.036.

Chen S., Hong X., Luk B.L., Harris C.J. (2009), Non-linear system identification using particle swarm optimisation tuned radial basis function models, International Journal of Bio-Inspired Computation, 1(4): 246–258, doi:10.1504/IJBIC.2009.024723.

Chen S., Wu Y., Luk B.L. (1999), Combined genetic algorithm optimization and regularized orthogonal least squares learning for radial basis function networks, IEEE Transactions on Neural Networks, 10(5): 1239–1243, doi: 10.1109/72.788663.

Chun-Tao M., Xiao-Xia L., Li-Yong Z. (2007), Radial basis function neural network based on ant colony optimization, IEEE International Conference on Computational Intelligence and Security Workshops (CISW 2007), pp. 59–62, doi: 10.1109/CISW.2007.4425446.

Ding S., Xu L., Su C., Jin F. (2012), An optimizing method of RBF neural network based on genetic algorithm, Neural Computing and Applications, 21(2): 333–336, doi: 10.1007/s00521-011-0702-7.

Du K.L., Swamy M.N.S. (2014), Radial basis function networks, [in:] Neural Networks and Statistical Learning, Springer Publishing Company, Inc., pp. 299–335, doi: 10.5555/2578631.

Dua D., Graff C. (2019), UCI Machine Learning Repository, Irvine, CA: University of California, School of Information and Computer Science, Connectionist Bench (sonar, mines vs. rocks), http://archive.ics.uci.edu/ml/datasets/Connectionist+Bench+(Sonar,+Mines+vs.+Rocks)

Faris H., Aljarah I., Mirjalili S. (2016), Training feedforward neural networks using multi-verse optimizer for binary classification problems, Applied Intelligence, 45(2): 322–332, doi: 10.1007/s10489-016-0767-1.

Faris H., Aljarah I., Mirjalili S., Samui P., Sekhar S., Balas V.E. (2017), Evolving radial basis function networks using moth-flame optimizer, [in:] Handbook of Neural Computation, P. Samui, S. Sekhar, V.E. Balas [Eds], Academic Press, pp. 537–550, doi: 10.1016/B978-0-12-811318-9.00028-4.

Fasshauer G.E., Zhang J.G. (2007), On choosing “Optimal” shape parameters for RBF approximation, Numerical Algorithms, 45(1): 345–368, doi: 10.1007/s11075-007-9072-8.

Gan M., Peng H., Dong X.P. (2012), A hybrid algorithm to optimize RBF network architecture and parameters for nonlinear time series prediction, Applied Mathematical Modeling, 36(7): 2911–2919, doi: 10.1016/j.apm.2011.09.066.

Gorman R.P., Sejnowski T.J. (1998), Analysis of hidden units in a layered network trained to classify sonar targets, Neural Networks, 1(1): 75–89, doi: 10.1016/0893-6080(88)90023-8.

Gutiérrez F.J., Zhao A. (2015), Common data set 2015, Kongsberg GeoAcoustics Ltd.

Ho Y.C., Pepyne D.L. (2002), Simple explanation of the No-Free-Lunch theorem and its implications, Journal of Optimization Theory and Applications, 115(3): 549–570, doi: 10.1023/A:1021251113462.

Horng M.H., Lee Y.X., Lee M.C., Liou R.J. (2012), Firefly metaheuristic algorithm for training the radial basis function network for data classification and disease diagnosis, [in:] Theory and New Applications of Swarm Intelligence, R. Parpinelli, H.S. Lopes [Eds], IntechOpen, Rijeka, pp. 115–132, doi: 10.5772/39084.

Hyontai S. (2011), Using quick decision tree algorithm to find better RBF networks, [In:] Intelligent Information and Database Systems. ACIIDS 2011. Lecture Notes in Computer Science, Nguyen N.T., Kim CG., Janiak A. (eds), Vol 6591. Springer, Berlin, Heidelberg, pp. 207–217, doi: 10.1007/978-3-642-20039-7_21.

Zhang D. et al. (2014), A new optimized GA-RBF neural network algorithm, Computational Intelligence and Neuroscience, 2014: article ID 982045, 6 pages, doi: 10.1155/2014/982045.

Khishe M., Aghababaee M. (2013), Identifying and controlling sonar clutter by clutter indelible metho, Iranian Conference on Electrical and Computer Engineering, pp. 523–529, Sarvestan, Shiraz.

Khishe M., Mosavi M. (2017), Active sonar data set, Mandeley Data, v1, doi: 10.17632/fyxjjwzphf.1

Khishe M., Mosavi M.R., Kaveh M. (2017), Improved Migration Models of Biogeography-based Optimization for Sonar Data Set Classification using Neural Network, Applied Acoustic, 118: 15–29, doi: 10.1016/j.apacoust.2016.11.012.

Lin C.L., Wang J., Chen C.Y., Chen C.W., Yen C. (2009), Improving the generalization performance of RBF neural networks using a linear regression technique, Expert Systems with Applications, 36(10): 12049–12053, doi: 10.1016/j.apacoust.2016.11.012.

Mirjalili S. (2016), SCA: a Sine Cosine Algorithm for solving optimization problems, Knowledge-Based Systems, 96: 120–133, doi: 10.1016/j.knosys.2015.12.022.

Mirjalili S., Hashim S.Z.M., Sardroudi H.M. (2012), Training feedforward neural networks using hybrid particle swarm optimization and gravitational search algorithm, Applied Mathematics and Computation, 218(22): 11125–11137, doi: 10.1016/j.amc.2012.04.069.

Mirjalili S., Mirjalili S.M., Lewis A. (2014), Let a biogeography-based optimizer train your multi-layer perceptron, Journal of Information Sciences, 269: 188–209, doi: 10.1016/j.ins.2014.01.038.

Mosavi M.R., Khishe M. (2017), Training a feed-forward neural network using particle swarm optimizer with autonomous groups for sonar target classification, Journal of Circuits, Systems, and Computers (JCSC), 26(11): 1750185:1-1750185:20, doi: 10.1142/S0218126617501857.

Mosavi M.R., Khishe M., Moridi A. (2016), Classification of sonar target using hybrid particle swarm and gravitational search, Marine Technology, 3(1): 1–13, https://www.sid.ir/en/journal/ViewPaper.aspx?id=532112.

Neruda R., Kudová P. (2005), Learning methods for radial basis function networks, Future Generation Computer Systems, 21(7): 1131–1142, doi: 10.1016/j.future.2004.03.013.

Nguyen L.S., Frauendorfer D., Mast M. S., Gatica-Perez D. (2014), Hire me: Computational inference of hirability in employment interviews based on nonverbal behavior, IEEE Transactions on Multimedia, 16(4): 1018–1031, doi: 10.1109/TMM.2014.2307169.

Park J., Sandberg I.W. (1993), Approximation and radial-basis-function networks, Neural Computation, 5(2): 305–316, doi: 10.1162/neco.1993.5.2.305.

Preston M. (2004), Resampling sonar echo time series primarily for seabed sediment, United State Patent, US 6,801,474 B2.

Vogt M. (1993), Combination of radial basis function neural networks with optimized learning vector quantization, IEEE International Conference on Neural Networks, San Francisco, CA, USA, Vol. 3, pp. 1841–1846, doi: 10.1109/ICNN.1993.298837.

Wu D. et al. (2010), Prediction of Parkinson’s disease tremor onset using a radial basis function neural network based on particle swarm optimization, International Journal of Neural Systems, 20(02): 109–116, doi: 10.1142/S0129065710002292.

Yang X.S. (2014), Nature-Inspired Optimization Algorithms, Elsevier.

Yu B., He X. (2006), Training radial basis function networks with differential evolution, Proceedings of IEEE International Conference on Granular Computing, pp. 369–372.

Yu H., Xie T., Paszczyński S., Wilamowski B.M. (2011), Advantages of radial basis function networks for dynamic system design, IEEE Transactions on Industrial Electronics, 58(12): 5438–5450, doi: 10.1109/TIE.2011.2164773.

Yu-Qing S., Jun-Fei Q., Hong-Gui H. (2016), Structure design for RBF neural network based on improved K-means algorithm, Chinese Control and Decision Conference (CCDC), Yinchuan, pp. 7035–7040, doi: 10.1109/CCDC.2016.7532265.

Zhong Y., Huang X., Meng P., Li F. (2014), PSO-RBF neural network PID control algorithm of electric gas pressure regulator, Abstract and Applied Analysis, 2014: article ID 731368, 7 pages, doi: 10.1155/2014/731368.




DOI: 10.24425/aoa.2020.135281