最优线性联想记忆网络的γ谱解析  

The γ spectrum analysis based on optimal linear associative memory networks

作　　者：钱晋[1] 王红艳[2] 史宏声[1] 舒康颖[1] 

机构地区：[1]中国计量学院材料科学与工程学院,浙江杭州310018 [2]中国原子能科学研究院,北京102413

出　　处：《中国计量学院学报》2013年第1期77-80,共4页Journal of China Jiliang University

摘　　要：利用最优线性联想记忆神经网络,对γ能谱进行了定性识别与定量分析,成功克服了传统解谱方法对操作人员要求高、运算速度慢、不能准确识别有重峰的复杂γ能谱等问题.采用全谱输入法,利用整个能谱的信息,降低了对探测器能量分辨率的要求,避免了寻峰、能量刻度与效率刻度,准确识别了单核素能谱与几种核素的混合能谱,从而成为一种行之有效的解谱手段,为高性能便携式探测器解谱软件的开发提供了依据.A qualitative identification and a quantitative analysis of the γ spectrum based on the use of optimal linear associative memory neural network were studied.Compared with the traditional unfolding methods,the OLAM network possesses the properties of low operating demands,high speed and accurate identification of double complex γ spectrum.The full spectrum input method which made use of the information of the entire energy spectrum reduced the requirements of energy resolution of the detector and avoided peak searching,energy calibration and efficiency calibration.This method provides a basis for the development of spectrum solution software of high-performance portable detectors and can be an effective means of spectrum analysis.

关 键 词：最优线性联想记忆网络 Γ谱 核素识别 全谱输入 

分 类 号：TL812.1[核科学技术—核技术及应用]