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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

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

Air Force Logistics University, Xuzhou, Jiangsu 221000, China

 PDF, 786 kB

DOI: 10.18287/2412-6179-CO-789

Страницы: 296-300.

Язык статьи: English

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.

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.


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