| 基于ELM和MA的微型四频天线设计 |
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| 引用本文： | 曾启明,纪震,李琰,俞航.基于ELM和MA的微型四频天线设计[J].电子学报,2014,42(9):1693-1698. |
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| 作者姓名： | 曾启明 纪震 李琰 俞航 |
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| 作者单位： | 1. 深圳大学信息工程学院, 广东深圳 518060;
2. 深圳大学计算机与软件学院, 广东深圳 518060;
3. 深圳市嵌入式系统设计重点实验室, 广东深圳 518060 |
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| 基金项目： | 国家自然科学基金(No ．61171125，No ．60872125，No ．61201042)；深圳市海外高层次人才创新创业专项资金(No ．KQC201108300044 A )；深圳市战略性新兴产业发展专项资金项目(No ．JCYJ20120613173154123)；国家-广东省联合自然科学基金 |
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| 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.
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| 关 键 词： | 四频天线 回波损耗 极限学习机 文化基因算法 综合学习粒子群优化算法 |
| 收稿时间： | 2013-09-06 |
A Miniature Four-Band Antenna Design Using ELM and MA |
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| ZENG Qi-ming,JI Zhen,LI Yan,YU Hang.A Miniature Four-Band Antenna Design Using ELM and MA[J].Acta Electronica Sinica,2014,42(9):1693-1698. |
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| Authors: | ZENG Qi-ming JI Zhen LI Yan YU Hang |
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| Institution: | 1. College of Engineering and Information, Shenzhen University, Shenzhen, Guangdong 518060, China;
2. College of Computer Science and Software Emgineering, Shenzhen University, Shenzhen, Guangdong 518060, China;
3. Shenzhen Key Laboratory of Embedded System Design, Shenzhen, Guangdong 518060, China |
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| Abstract: | 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 antenna.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. |
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| Keywords: | four-band antenna return loss extreme learning machine memetic algorithm CLPSO |
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