Отрывок: The GRNN model is initialized. To further enhance the performance of GRNN, the opti- mal value of is obtained through IFOA and substituted into the GRNN model to recognize HRRP, and the recognition result is output. 3. Simulation experiment 3.1. Experimental data The experimental environment was windows7 operat- ing system, 4 GB memory, 320 GB disk, and In- tel(R)Pentium(R)G630 CPU. The simulation experiment was carried out on the MATLAB pla...
Название : | Application of the fruit fly optimization algorithm to an optimized neural network model in radar target recognition |
Авторы/Редакторы : | Liu, M. Sun, Z.H. |
Ключевые слова : | radar technology target recognition generalized regression neural network high-resolution range profile fruit fly optimization algorithm |
Дата публикации : | Апр-2021 |
Издательство : | Самарский национальный исследовательский университет имени акад. С.П. Королева |
Библиографическое описание : | 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. |
Серия/номер : | 45;2 |
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. |
URI (Унифицированный идентификатор ресурса) : | https://dx.doi.org/10.18287/2412-6179-CO-789 http://repo.ssau.ru/handle/Zhurnal-Komputernaya-optika/Application-of-the-fruit-fly-optimization-algorithm-to-an-optimized-neural-network-model-in-radar-target-recognition-88403 |
Другие идентификаторы : | Dspace\SGAU\20210503\88403 |
Располагается в коллекциях: | Журнал "Компьютерная оптика" |
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450217.pdf | Основная статья | 767.44 kB | Adobe PDF | Просмотреть/Открыть |
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