A new efficient approach for fast and accurate design of frequency selective surfaces based on geometry estimation networks

A new efficient approach for fast and accurate design of frequency selective surfaces based on... Artificial neural networks are a favorite modern tool for high accuracy designing frequency selective surfaces (FSSs) in RF and microwave engineering field. In this paper, a new fast and precise ANN-based algorithm for designing FSSs is presented. This algorithm, unless the previous works, can develop the structures with due attention to features of incident waves and improve the applicability of developed FSSs. For achieving this algorithm, at first, a new method is presented for the better preparation of training datasets, called frequency sweep method (FSM). The advantage of FSM is to reduce the size of training datasets and prevent from superfluous simulations. So the time needed for the preparation of training datasets and to train the networks is less than before. Following that, FSM is used to train geometry estimation ANN (GEANN) with primary goal of FSSs design in little time and without any optimization algorithm. The proposed design procedure is complete design and analysis unit that consisted of a sequence of GEANN and traditional response calculation ANNs (RCANNs). GEANN is used to estimate geometric dimensions of FSSs with desired incident wave, and RCANNs are used to calculate the frequency response of FSSs under other various incident waves. The results show that required time for designing FSS is less than 30 ms, and errors are <1 %. Both analytical and experimental results confirm the correctness of predicted values. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Neural Computing and Applications Springer Journals

A new efficient approach for fast and accurate design of frequency selective surfaces based on geometry estimation networks

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Publisher
Springer London
Copyright
Copyright © 2016 by The Natural Computing Applications Forum
Subject
Computer Science; Artificial Intelligence (incl. Robotics); Data Mining and Knowledge Discovery; Probability and Statistics in Computer Science; Computational Science and Engineering; Image Processing and Computer Vision; Computational Biology/Bioinformatics
ISSN
0941-0643
eISSN
1433-3058
D.O.I.
10.1007/s00521-016-2221-z
Publisher site
See Article on Publisher Site

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