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A simple and efficient approach to train artificial neural networks using a genetic algorithm to calculate the resonant frequency of an RMA on thick substrate
Author(s) -
Khuntia Bonomali,
Pattnaik Shyam S.,
Panda Dhruba C.,
Neog Dipak K.,
Devi S.,
Dutta Malay
Publication year - 2004
Publication title -
microwave and optical technology letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.304
H-Index - 76
eISSN - 1098-2760
pISSN - 0895-2477
DOI - 10.1002/mop.20126
Subject(s) - artificial neural network , genetic algorithm , simple (philosophy) , backpropagation , microwave , antenna (radio) , electromagnetics , field (mathematics) , microstrip , algorithm , electronic engineering , computer science , engineering , artificial intelligence , electrical engineering , machine learning , telecommunications , mathematics , philosophy , epistemology , pure mathematics
Abstract Both genetic algorithms (GAs) and artificial neural networks (ANNs) have been used in the field of computational electromagnetics as the most powerful optimizing tools. In this paper, a simple and efficient method is presented to handle the problem of competing convention while training an ANN by using a GA. This technique is applied to calculate the resonant frequency of a thick‐substrate rectangular microstrip antenna (RMA). The training time is less than that of a normal feed‐forward backpropagation algorithm. The measured results are in very good agreement with experimental results. © 2004 Wiley Periodicals, Inc. Microwave Opt Technol Lett 41: 313–315, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.20126