为实现对双陷波超宽带(UWB)天线的精准神经网络建模,提出了一种利用改进的果蝇算法(FOA)优化广义回归神经网络(GRNN)的建模方法。该方法通过扩大果蝇搜索范围,在味道判定公式中引入调整项来实现果蝇算法的改进,并用改进后的果蝇算法优化GRNN的光滑因子。这样可以避免果蝇算法陷入局部最优,提高模型预测精度。将该方法用于双陷波超宽带天线模型的建立中,并对天线的S11参数和电压驻波比VVSWR参数进行预测。结果表明,相比于FOA-GRNN建模方法和GRNN建模方法,S11参数的最大相对误差分别减小了91.08%和99.14%;VVSWR参数的最大相对误差分别减小了98.36%和99.18%,使超宽带天线建模精度得到提高,验证了该方法的可行性。

In order to realize accurate neural network modeling for the dual notch ultra-wideband(UWB)antenna, a modeling method using the improved fruit fly algorithm (FOA) to optimize the generalized regression neural network (GRNN) is proposed. This method achieves the improvement of the fruit fly algorithm by expanding the search range of fruit flies, introducing adjustment items into the taste judgment formula, and using the improved fruit fly algorithm to optimize the smoothing factor of GRNN. In this way, the fruit fly algorithm can be prevented from falling into local optimum and the model prediction accuracy can be improved. This method is used in the establishment of the dual notch UWB antenna model, and the antenna S₁₁ parameters and voltage standing wave ratio V_VSWR parameters are predicted. Experimental results show that, compared with the FOA-GRNN modeling method and the GRNN modeling method, the maximum relative error of the S₁₁ parameter is reduced by 91.08% and 99.14%, respectively, and the maximum relative error of the V_VSWR parameter is reduced by 98.36% and 99.18%, respectively. The accuracy of UWB antenna modeling is improved, and the method feasibility is verified.