Nuclide identification algorithm based on K–L transform and neural networks

Traditional spectrum analysis algorithm based on peak search is hard to deal with complex overlapped peaks, especially in bad resolution and high background conditions. This paper described a new nuclide identification method based on the Karhunen–Loeve transform (K–L transform) and artificial neural networks. By the K–L transform and feature extraction, the nuclide gamma spectrum was compacted. The K–L transform coefficients were used as the neural networkʹs input. The linear associative memory and ADALINE were discussed. Lots of experiments and tests showed that the method was credible and practical, especially suitable for fast nuclide identification.