Multi-Objective Intelligent Optimization Design Method of Microstrip Antenna Based on Back Propagation Neural Network

IF 0.6 4区 工程技术 Q4 ENGINEERING, ELECTRICAL & ELECTRONIC Journal of Nanoelectronics and Optoelectronics Pub Date : 2024-07-01 DOI:10.1166/jno.2024.3626
Dingli Liu, Yang Yue, Guilin Xu, Yu Xian
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Abstract

Antenna plays an important role in modern communication. Accurate calculation of antenna structure to obtain reasonable electromagnetic characteristic parameters is an important part of antenna design. With the increasing complexity of the antenna structure, a large number of numerical calculations are required in the design process to determine the optimal structure size. Therefore, it’s necessary to study the fast optimization algorithm of antenna multi-objective (antenna bandwidth, gain, polarization characteristics, etc) optimization design method. In this paper, a new adaptive BF neural network is proposed to optimize the resonant frequency and size structure of dual frequency circular polarization microstrip antenna, so as to improve the efficiency of antenna design.
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基于反向传播神经网络的微带天线多目标智能优化设计方法
天线在现代通信中发挥着重要作用。精确计算天线结构以获得合理的电磁特性参数是天线设计的重要组成部分。随着天线结构的日益复杂,在设计过程中需要进行大量的数值计算来确定最佳结构尺寸。因此,有必要研究天线多目标(天线带宽、增益、极化特性等)优化设计方法的快速优化算法。本文提出了一种新的自适应 BF 神经网络来优化双频圆极化微带天线的谐振频率和尺寸结构,从而提高天线设计的效率。
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来源期刊
Journal of Nanoelectronics and Optoelectronics
Journal of Nanoelectronics and Optoelectronics 工程技术-工程:电子与电气
自引率
16.70%
发文量
48
审稿时长
12.5 months
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