矩形微带天线谐振频率的粒子群神经网络建模

内容简介：谐振频率是微带天线设计过程中最重要的一个参数,直接决定设计的成败.文章提出一种改进的前向神经网络模型,该模型采用粒子群优化算法进行训练,网络结构可以根据被建模问题的复杂程度进行自适应调整,进而得到泛化能力好的神经网络.通过这种改进的粒子神经网络对矩形微带天线的谐振频率进行建模,得到的结果明显优于该问题已有文献的结果,可见这种粒子群神经网络对此问题的有效性.基于粒子群神经网络的矩形微带天线谐振频率模型可以明显提高微带天线的计算机辅助设计水平.

Abstract：Resonant frequency is an important parameter in the design process of microstrip antenna (MSA). An improved feedforward artificial neural network (ANN) that is trained by the algorithm of particle swarm optimization ( PSO) is proposed in this paper. Structure of the ANN can be adjusted adaptively according to the complexity of mapped problem,which means its generalization is good. The resonant frequency of rectangular MSA is modeled based on the improved ANN,and the result is better than available ones obviously. It shows the ANN is effective. The model based on PSO ANN can speed the design of MSA. Furthermore, it improves the level of CAD of MSA.

作者：田雨波, 李正强, 王建华,

关键词：微带天线, 神经网络, 粒子群优化, 谐振频率,