一种基于人工神经网络的RFID标签天线优化方法

Jiachuan Shang, Ning Zhang, Xiuping Li
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引用次数: 1

摘要

将进化算法与人工神经网络(ANN)相结合,应用于RFID标签天线优化平台。提出了一种有效的RFID标签天线优化方法,即粒子群优化算法(PSO)或遗传算法(GA)与人工神经网络相结合。利用人工神经网络建立了标签天线的非线性模型,该模型的精度与电磁模拟器相当,可用于构造粒子群算法和遗传算法的适应度函数。PSO和GA优化器是用c++开发和执行的。最后,该优化方法比任何电磁模拟器优化方法都要有效得多。此外,粒子群优化结果表明,它比遗传算法更快。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An effective method for RFID tag antenna optimization based on artifical neural network
Evolutionary algorithms combined with artificial neural network (ANN) have been applied in RFID tag antenna optimization platform. An effective method for RFID tag antenna optimization by particle swarm optimization (PSO) algorithm or genetic algorithm (GA) combined with ANN is presented in this paper. ANN is used to establish the non-linear model of tag antenna which is shown to be as accurate as an electromagnetic simulator and can be used for constructing the fitness function of PSO and GA. The PSO and GA optimizers are developed and executed in C++. Finally, this optimization method is turned out to be much more efficient than any electromagnetic simulator optimization. In addition, the PSO optimization results show that it is faster than GA.
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