Anti-noise performance of the pulse coupled neural network applied in discrimination of neutron and gamma-ray

Abstract

Abstract In this study, the anti-noise performance of a pulse-coupled neural network (PCNN) was investigated in the neutron and gamma-ray ( n − $$\gamma$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>γ</mml:mi> </mml:math> ) discrimination field. The experiments were conducted in two groups. In the first group, radiation pulse signals were pre-processed using a Fourier filter to reduce the original noise in the signals, whereas in the second group, the original noise was left untouched to simulate an extremely high-noise scenario. For each part, artificial Gaussian noise with different intensity levels was added to the signals prior to the discrimination process. In the aforementioned conditions, the performance of the PCNN was evaluated and compared with five other commonly used methods of n − $$\gamma$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>γ</mml:mi> </mml:math> discrimination: (1) zero crossing, (2) charge comparison, (3) vector projection, (4) falling edge percentage slope, and (5) frequency gradient analysis. The experimental results showed that the PCNN method significantly outperforms other methods with outstanding $$\mathrm{FoM}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>FoM</mml:mi> </mml:math> -value at all noise levels. Furthermore, the fluctuations in $$\mathrm{FoM}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>FoM</mml:mi> </mml:math> -value of PCNN were significantly better than those obtained via other methods at most noise levels and only slightly worse than those obtained via the charge comparison and zero-crossing methods under extreme noise conditions. Additionally, the changing patterns and fluctuations of the $$\mathrm{FoM}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>FoM</mml:mi> </mml:math> -value were evaluated under different noise conditions. Hence, based on the results, the parameter selection strategy of the PCNN was presented. In conclusion, the PCNN method is suitable for use in high-noise application scenarios for n − $$\gamma$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>γ</mml:mi> </mml:math> discrimination because of its stability and remarkable discrimination performance. It does not rely on strict parameter settings and can realize satisfactory performance over a wide parameter range.