Genetic algorithm based selective neural network ensemble method to analyse rectangular microstrip antenna

Navreet Saini, B. S. Dhaliwal, Simranjit Kaur Josan
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引用次数: 3

Abstract

Harmony in variety i.e. unity without similarity is a concept inspired from ancient times. Thinkers propose a team approach based on the same concept for problem solving i.e. using a combined group of solvers to resolve a difficult problem. Neural network ensemble (NNE) is a concept based on the same approach. Multiple artificial neural networks (ANNs) are trained for the same dataset to give the appropriate measured resonant frequency from the relative parameters of rectangular microstrip antenna (MSA). The previous experimental works' MSA datasets have been used for training of ANNs. Genetic Algorithm (GA) is employed to compute the optimum subset of ANNs which perform better than rest available to constitute an ensemble. A model of resonant frequency of MSA is established by using this NNE approach and the results have been compared with some previous works.
基于遗传算法的选择性神经网络集成方法分析矩形微带天线
“和而不同”即“统一而不相似”是一个源自古代的概念。思考者提出了一种基于相同概念的解决问题的团队方法,即使用一组解决者来解决难题。神经网络集成(NNE)是基于相同方法的概念。针对同一数据集训练多个人工神经网络,根据矩形微带天线(MSA)的相关参数给出相应的测量谐振频率。以前的实验作品的MSA数据集已用于人工神经网络的训练。采用遗传算法(GA)计算性能优于其他可用人工神经网络的最优子集,从而构成一个集成。利用该方法建立了MSA的谐振频率模型,并与前人的研究结果进行了比较。
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