Fast nuclide identification with higher accuracy rate is a significant requirement within nuclear applications. So effective spectrum identification algorithms are developed for low resolution spectra of NaI(TI) scintillator and Si(Li) detectors. Identification of X-ray and gamma complex spectra is the main objective of current research. So the experimental gamma and X-ray data are trained and tested using artificial neural network (ANN), support vector machine (SVM) and similarity classifiers. The features of acquired X-ray and gamma spectra are extracted using eight algorithms. These algorithms depend on fusion of time-domain descriptors (FTDD), electromyography (EMG), multiscale wavelet packet (MWP), multiscale wavelet packet with statistics (MWPS), principle component analysis (PCA), multi-dimensional scale, the preserved linear projection (LPP) and diffusion map. Robustness of these algorithms is investigated in terms of noise degradations such as Gaussian, Rician, Rayleigh and other complex degradations. Classification accuracy is investigated with the source name. The recognized spectrum is analyzed from view of peak width calibration, efficiency calibration, sum peak analysis, peak-to-Compton ratio (PCR). The rapid identification process is conducted with the algorithm based on electromyography method for both gamma and X-ray spectra. However, the algorithm based on diffusion map realizes the slowest spectrum identification. It is concluded that, for gamma and X-ray spectra, the SVM classifier achieves the fastest identification with maximum rate of 99%. Finally, the ANN is observed to achieve better rate of 100% with slower identification process depending on FTDD, EMG, LPP methods. The proposed approach helps the realization of fastest spectrum identification and classification of gamma and X-ray spectra within nuclear applications with higher robustness and accuracy.

在核应用中，具有较高准确率的快速核素鉴定是一项重要要求。因此，针对NaI（TI）闪烁体和Si（Li）探测器的低分辨率光谱，开发了有效的光谱识别算法。鉴定X射线和伽玛复合光谱是当前研究的主要目标。因此，使用人工神经网络（ANN），支持向量机（SVM）和相似度分类器对实验的伽玛和X射线数据进行训练和测试。使用八种算法提取获得的X射线和伽玛光谱的特征。这些算法取决于时域描述符（FTDD），肌电图（EMG），多尺度小波包（MWP），具有统计量的多尺度小波包（MWPS），主成分分析（PCA），多维尺度，保留的线性投影（LPP）和扩散图。这些算法的鲁棒性是根据诸如高斯，里斯安，瑞利和其他复杂降级的噪声降级进行研究的。使用源名称调查分类准确性。从峰宽校准，效率校准，总峰分析，峰与康普顿比（PCR）的角度分析识别的光谱。利用基于肌电图方法的算法对γ和X射线光谱进行快速识别。然而，基于扩散图的算法实现了最慢的频谱识别。结论是，对于伽玛和X射线光谱，SVM分类器以99％的最大速率实现了最快的识别。最后，取决于FTDD，EMG，LPP方法，观察到的ANN可以达到100％的较高识别率，并且识别过程较慢。所提出的方法有助于在核应用中以更高的鲁棒性和准确性实现最快的光谱识别以及伽马和X射线光谱的分类。