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Electronics and Telecommunications Research Institute (한국전자통신연구원)
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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)
  • Received : 2012.02.05
  • Accepted : 2012.06.01
  • Published : 2012.10.31
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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

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