Application of the fruit fly optimization algorithm to an optimized neural network model in radar target recognition

With the development of computer technology, there are more and algorithms models for data processing analysis, which brings a new direction to radar target recognition. This study mainly analyzed recognition high resolution range profile (HRRP) in applied generalized regression neural network (GRNN) model HRRP In order improve performance HRRP, fruit fly optimization algorithm (FOA) was improved optimize parameters GRNN model. Simulation experiments were carried out on three types aircraft. The FOA-GRNN (IFOA-GRNN) compared with radial basis function (RBF) models. results showed that IFOA-GRNN had better convergence accuracy, highest average rate (96.4 %), shortest calculation time (275 s), good under noise interference. experimental show has can be further promoted practice.