In the design of dula band-notched ultra-wide band antennas, the direct inverse neural network model has lower precision, the BP inverse neural network has poor generalization ability, and if only use the HFSS simulation software, it is necessary to continuously optimize the antenna parameters which increasing the design time. Aiming at the above problems, this paper proposed a method combining HFSS with sparse regularized inverse neural netwrok. This method added l_1/2 norm and l₂ norm in the performance function of the inverse neural network. The l_1/2 norm introduced a new weight coefficient, expanded the input sample vector, made the network to obtain the sparsity solution more easily, and the inverse model got higher accuracy. Meanwhile, the l₂ norm avoided the over-fitting phenomenon effectively and made the network generalization ability stronger. It applied to the design of dual band-notched ultra-wide band antenna, using of arc grooves on radiating patches generated notch characteristics, and according to the antenna target voltage standing wave ratio(VSWR) solved inversely the corresponding slot size. Simulation results show that the relative error of slot angle which corresponding to VSWR of the antenna reduced by 69.3%, and the relative error of slot radius lessened by 88.7%, and the network running time decreased by 15.9% compared with BP neural network method. The final designed antenna bandwidth is 2.4~11 GHz, achieves the good notch characteristics in 3.31~3.8 GHz and 4.98~6.05 GHz, and shortens the entire antenna design cycle.

[2019-08-05] Printed Article /doc/2018.02.0109/v2