Abstract: For hadron calorimeters with a transverse structure there exists a possibility to reconstruct the particles energy with a better resolution using the neural network algorithm. For the calorimeter with a novel longitudinal structure that capability of the neural network method for the better determination of the particles energy in comparison with the traditional method was studied. The research is based on the information from the experiment at IHEP (Serpukhov) with the test $pi$+$MIN$/-beam with energies 10, 20, 30 and 40 GeV. Using of the neural network improve the energy resolution of a system of electromagnetic calorimeter and hadron calorimeter with scintillators parallel to beam.!4
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机译：摘要：对于具有横向结构的强子量热仪，有可能使用神经网络算法以更好的分辨率重建粒子能量。对于具有新型纵向结构的量热仪，研究了与传统方法相比神经网络方法能够更好地确定粒子能量的能力。该研究基于IHEP（Serpukhov）的实验信息，能量为10、20、30和40 GeV的$ pi $ + $ MIN $ /束测试。神经网络的使用提高了具有与光束平行的闪烁体的电磁量热仪和强子量热仪系统的能量分辨率！4
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