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Application of the fruit fly optimization algorithm to an optimized neural network model in radar target recognition
M. Liu 1, Z.H. Sun 2

1 Sichuan Vocational College of Chemical Technology, Luzhou, Sichuan 646005, China,

2 Air Force Logistics University, Xuzhou, Jiangsu 221000, China

 PDF, 786 kB

DOI: 10.18287/2412-6179-CO-789

Страницы: 296-300.

Язык статьи: English

Аннотация:
With the development of computer technology, there are more and more algorithms and models for data processing and analysis, which brings a new direction to radar target recognition. This study mainly analyzed the recognition of high resolution range profile (HRRP) in radar target recognition and applied the generalized regression neural network (GRNN) model for HRRP recognition. In order to improve the performance of HRRP, the fruit fly optimization algorithm (FOA) algorithm was improved to optimize the parameters of the GRNN model. Simulation experiments were carried out on three types of aircraft. The improved FOA-GRNN (IFOA-GRNN) model was compared with the radial basis function (RBF) and GRNN models. The results showed that the IFOA-GRNN model had a better convergence accuracy, the highest average recognition rate (96.4 %), the shortest average calculation time (275 s), and a good recognition rate under noise in-terference. The experimental results show that the IFOA-GRNN model has a good performance in radar target recognition and can be further promoted and applied in practice.

Ключевые слова:
radar technology, target recognition, generalized regression neural network, high-resolution range profile, fruit fly optimization algorithm.

Citation:
Liu M, Sun ZH. Application of the fruit fly optimization algorithm to an optimized neural network model in radar target recognition. Computer Optics 2021; 45(2): 296-300. DOI: 10.18287/2412-6179-CO-789.

Литература:
  1. Shu T, Tang B, Yin K, Sun Q, Chen Y, Yu W. Development of multichannel real-time Hardware-in-the-Loop radar environment simulator for missile-borne Synthetic Aperture Radar. Biochem J 2015; 40(2): 368-373.
  2. Kang M, Ji K, Leng X, Xing X, Zou H. Synthetic aperture radar target recognition with feature fusion based on a stacked autoencoder. Sensors 2017; 17(1): 192.
  3. Karine A, Toumi A, Khenchaf A, Hassouni ME. Target recognition in radar images using weighted statistical dictionary-based sparse representation. IEEE Geosci Remote Sens Lett 2017; 14(12): 2403-2407.
  4. Xiong W, Zhang G, Liu S, Yin J. Multiscale kernel sparse coding-based classifier for HRRP radar target recognition. IET Radar Sonar Navig 2016; 10(9): 1594-1602.
  5. Lee KC. Radar target recognition by machine learning of K-nearest neighbors regression on angular diversity RCS. Appl Comput Electromagn Soc J 2019; 34(1): 75-81.
  6. Feng B, Chen B, Liu H. Radar HRRP target recognition with deep networks. Patt Recogn 2017; 61(Complete): 379-393.
  7. Karine A, Toumi A, Khenchaf A, Hassouni ME. Radar target recognition using salient keypoint descriptors and multitask sparse representation. Remote Sens 2018; 10(6): 843.
  8. Guo Y, Xiao H, Kan Y, Fu Q. Learning using privileged information for HRRP-based radar target recognition. IET Signal Process 2018; 12(2): 188-197.
  9. Specht DF. A general regression neural network. IEEE Trans Neural Netw 1991; 2(6): 568-576.
  10. Xing B, Gao W-J. Fruit fly optimization algorithm. In Book: Xing B, Gao W-J. Innovative computational intelligence: A rough guide to 134 clever algorithms. Switzerland: Springer International Publishing; 2014: 167-170.
  11. Cecati C, Kolbusz J, Rozycki P, Siano P, Wilamowski BM. A Novel RBF training algorithm for short-term electric load forecasting and comparative studies. IEEE Trans Ind Electron 2015; 62(10): 6519-6529.
  12. Pierre OC. GRNN: General regression neural network. Revue de Physique Appliquée 2013, iv(6): 1321-1325.
  13. Wang F, Devabhaktuni VK, Xi C, Zhang Q. Neural network structures and training algorithms for RF and microwave applications. Int J RF Microw C E 2015; 9(3): 216-240.
  14. Chi P, Li S, Xu C, Zhang T, Zhao J, Liu Y, Wang Y, Xie Y. PRIME: A Novel processing-in-memory architecture for neural network computation in ReRAM-based main memory. Computer Architecture News 2016; 44(3): 27-39.
  15. Moeskops P, Viergever MA, Mendrik AM, de Vries LS, Benders MJNL, Išgum I. Automatic segmentation of MR brain images with a convolutional neural network. IEEE Trans Med Imaging 2017; 35(5): 1252-1261.
  16. Ben Ali J, Fnaiech N, Saidi L, Chebel-Morello B, Fnaiech F. Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration signals. Appl Acoust 2015; 89: 16-27.
  17. Yu Y, Wang YR, Gao SC, Zheng T. Statistical modeling and prediction for tourism economy using dendritic neural network. Comput Intell Neurosci 2017; 2017: 1-9.

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