一种基于小波包分解的神经网络识别γ能谱方法

γ-ray spectrum identification with neural network based on wavelet packet decomposition

提出了一种基于小波包分解的神经网络识别γ能谱方法,该方法将γ能谱看作非平稳离散信号,对γ能谱做小波包分解得到各频带的能量,以各频带能量为元素构造特征向量作为神经网络的训练样本,利用神经网络的分类功能实现γ能谱的识别。结果表明,该方法不仅能准确地识别不同种类标准源的γ能谱,还能准确识别不同批次标准源的γ能谱,具有很好的实用价值。

A new method was developed to identify γ-ray spectra with neural network based on wavelet packet decomposition, γ-ray spectra are regarded as non-stationary discrete signals. By using wavelet packet decomposition of γ-ray spectra, characteristic vector of the energy over all frequency band is obtained and used as an input variable into neural network to identify γ-ray spectra results of applying the method to γ-ray sources shows that the method can identify accurately γ-ray spectra from standard sources of different species and batches.