基于ELM和MA的微型四频天线设计

摘要

提出一个基于极限学习机ELM（Extreme Learning Machine）和文化基因算法MA（Memetic Algorithm）的微型四频（0.92/2.4/3.5/5.8GHz）天线设计算法AntMA-ELM ．为了提高天线的性能，算法在MA框架下引入基于综合学习粒子群优化算法CLPSO （Comprehensive Learning Particle Swarm Optimizer ）全局搜索和DSCG （Davies ，Swann ，and Campey with Gram-schmidt ）局部搜索，用于确定天线的几何参数．同时，建立ELM回归模型用于直接评估MA优化的适应值函数．实验结果表明，ELM回归模型能够根据输入参数正确估算天线的回波损耗，使MA算法有效提高设计性能和加速优化过程．天线在四个目标频段的回波损耗值均优于-10dB ，满足设计要求．%This paper proposes an extreme learning machine (ELM) and memetic algorithm (MA) based miniature four-band (0.92/2.4/3.5/5.8GHz ) antenna design algorithm namely the AntMA-ELM .It combines a comprehensive learning particle swarm optimizer (CLPSO ) based global search and a DSCG (Davies ,Swann ,and Campey with Gram-schmidt ) orthogonalization based local search in the MA framework to form a novel optimization algorithm for the geometrical parameters selection of the an-tenna .An ELM based regression model is introduced to estimate antenna performance ,and accelerate the search speed .Experimental results show that the AntMA-ELM obtains promising performance with short computational time .Particularly ,the return losses at all targeted frequency bands are smaller than -10dB .