一个优化的SVR算法用于脉冲堆叠校正在脉冲形状歧视
Xianghe Liu1,2, Bingqi Liu2,3, Mingzhe Liu1,2
1The College Nuclear Technology and Automation Engineering, Chengdu University of Technology, Chengdu 610059, China.
这项研究引入了一个优化的支持向量回归 (SVR) 算法来纠正辐射检测中的脉冲堆积. 该方法在具有挑战性的环境中提高了中子-马脉冲形状区分的准确性.
科学领域:
- 核物理和辐射检测仪器仪表.
背景情况:
- 脉冲堆积在核辐射测量中显著扭曲信号,阻碍了准确的中子-马脉冲形状歧视.
- 现有的方法与脉冲扭曲作斗争,导致混合辐射场的识别精度降低.
研究的目的:
- 开发和验证一个优化的支向量回归 (SVR) 算法,以有效地纠正脉冲堆积.
- 为了提高中子-马脉冲形状歧视在高干扰环境中的准确性和可靠性.
主要方法:
- 开发了一个优化的支持向量回归 (SVR) 算法,集成了虫优化器 (DBO) 和鱼优化算法 (WOA).
- 使用电荷比较方法 (CCM) 评估性能,以进行脉冲形状歧视,并分析模拟的脉冲堆积特征.
- 实验是在混合中子-马辐射场中使用塑料闪器进行的.
主要成果:
- 拟议的DBO-WOA优化SVR算法有效地纠正了脉冲堆积,显著改善了中子-马区分.
- 该方法在识别中子和玛辐射方面表现出高精度,即使有重叠脉冲.
- 实验结果证实了脉冲形状歧视的增强忠实性和整体系统可靠性.
结论:
- 优化的SVR算法为核辐射检测中的脉冲堆积校正提供了强大的解决方案.
- 这一进步提高了在复杂,高干扰环境中运行的辐射检测系统的可靠性.
- 该研究强调了核仪器仪表中先进优化算法的潜力,以提高测量准确度.
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