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http://dx.doi.org/10.4218/etrij.12.0112.0088

ANN Synthesis Models Trained with Modified GA-LM Algorithm for ACPWs with Conductor Backing and Substrate Overlaying  

Wang, Zhongbao (School of Information Science and Technology, Dalian Maritime University)
Fang, Shaojun (School of Information Science and Technology, Dalian Maritime University)
Fu, Shiqiang (School of Information Science and Technology, Dalian Maritime University)
Publication Information
ETRI Journal / v.34, no.5, 2012 , pp. 696-705 More about this Journal
Abstract
Accurate synthesis models based on artificial neural networks (ANNs) are proposed to directly obtain the physical dimensions of an asymmetric coplanar waveguide with conductor backing and substrate overlaying (ACPWCBSO). First, the ACPWCBSO is analyzed with the conformal mapping technique (CMT) to obtain the training data. Then, a modified genetic-algorithm-Levenberg-Marquardt (GA-LM) algorithm is adopted to train ANNs. In the algorithm, the maximal relative error (MRE) is used as the fitness function of the chromosomes to guarantee that the MRE is small, while the mean square error is used as the error function in LM training to ensure that the average relative error is small. The MRE of ANNs trained with the modified GA-LM algorithm is less than 8.1%, which is smaller than those trained with the existing GA-LM algorithm and the LM algorithm (greater than 15%). Lastly, the ANN synthesis models are validated by the CMT analysis, electromagnetic simulation, and measurements.
Keywords
ACPWs; synthesis models; ANN; CMT; modified GA-LM algorithm;
Citations & Related Records
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Times Cited By Web Of Science : 0  (Related Records In Web of Science)
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