PSO-RBF神经网络优化一种标签缝隙天线

Study of Tag Slot Antenna Based on PSO-RBF Network Optimized Application

为天线匹配阻抗与结构参数之间的优化设计,提出RBF神经网络用于建立对称弯折缝隙天线匹配阻抗与几何结构参数之间的多变量、非线性模型;解决了使用全波分析工具计算所耗时间长的问题。进而采用粒子群算法对不同类型的阻抗,快速的找出小型化的尺寸结构,实现匹配阻抗与几何尺寸的智能优化。优化的结果基于HFSS软件测试,实际效果满足设计要求。为天线开发者提供了一种简洁易懂、操作简单方便的优化方法,以提高天线的设计效率;从而实现了计算机辅助一类标签天线的智能优化设计。

In order to optimization the design parameters between the antenna matching impedance and structural size, in this paper RBF neural network is used to establish the multivariate nonlinear model of symmetric bending slot antenna between matching impedance and geometric structure parameters;solved long time consuming problem while using full wave analysis tool to calculate. Then the particle swarm optimization algorithm is adopted to quickly found the size of the miniaturization structure with different types of impedance; realize the matching impedance and geometric size of the intelligent optimization. Based on the HFSS software testing, the result of the optimization actual achieve the design ’s requirements. It provides the antenna developers a concise and easy to understand、simple operation and convenient optimization method, to improve the efficiency of antenna design, so as to realize the computer aided intelligent optimization design of tag antennas.