Open AccessANN Synthesis Models Trained with Modified GA‐LM Algorithm for ACPWs with Conductor Backing and Substrate Overlaying
Author(s) -
Wang Zhongbao,
Fang Shaojun,
Fu Shiqiang
Publication year - 2012
Publication title -
etri journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.295
H-Index - 46
eISSN - 2233-7326
pISSN - 1225-6463
DOI - 10.4218/etrij.12.0112.0088
Subject(s) - conductor , genetic algorithm , algorithm , fitness function , artificial neural network , mean squared error , overlay , approximation error , function (biology) , computer science , error function , engineering , mathematics , artificial intelligence , machine learning , geometry , statistics , evolutionary biology , biology , programming language
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.