| 基于人工神经网络的核素识别方法 |
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| 引用本文： | 贺,楠.基于人工神经网络的核素识别方法[J].兵工自动化,2022,41(3):91-96. |
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| 作者姓名： | 贺 楠 |
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| 作者单位： | 西南科技大学国防科技学院,四川 绵阳 621010;中国兵器装备集团自动化研究所有限公司,四川 绵阳 621000,西南科技大学国防科技学院,四川 绵阳 621010,陆军装备部驻重庆地区军事代表局驻成都地区第一军代室,成都,611930 |
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| 基金项目： | 四川省科技厅自然科学基金(2019ZDZX0027, 2019YFG0514, 2020YFG0147) |
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| 摘 要： | 针对传统核素识别方法不具有强适应性导致识别率降低的问题，建立基于反向传播(back propagation，BP)
神经网络的核素识别预测模型。以镅、镉、钚、氡、钯、钴、铯7 种核素的实测信号为例进行仿真模拟，建立核素
识别模型。结果表明：该模型能快速准确地识别上述核素，应用前景广泛。
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| 关 键 词： | BP神经网络 核素 核素识别 信号 |
| 收稿时间： | 2021/12/23 0:00:00 |
| 修稿时间： | 2022/1/4 0:00:00 |
Nuclide Identification Method Based on Artificial Neural Network |
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| He Nan,LYU Huiyi,Wang Bo,He Rong,Zhu Wenkun,Yuan Changying.Nuclide Identification Method Based on Artificial Neural Network[J].Ordnance Industry Automation,2022,41(3):91-96. |
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| Authors: | He Nan LYU Huiyi Wang Bo He Rong Zhu Wenkun Yuan Changying |
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| Abstract: | In order to solve the problem that the recognition rate of traditional nuclide recognition methods is low due to
the lack of strong adaptability, a nuclide recognition prediction model based on back propagation (BP) neural network is
established. Taking the measured signals of seven nuclides of Americium, Cadmium, Plutonium, Radon, Palladium, Cobalt
and Cesium as an example, the nuclide identification model is established. The results show that the recognition model can
quickly and accurately identify the above nuclides, and has a broad application prospect. |
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| Keywords: | BP neural network nuclide identification nuclide signal |
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