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Adaptive neuro‐fuzzy models for the quasi‐static analysis of microstrip line
Author(s) -
Yildiz Celal,
Guney Kerim,
Turkmen Mustafa,
Kaya Sabri
Publication year - 2008
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.23322
Subject(s) - adaptive neuro fuzzy inference system , artificial neural network , microstrip , computer science , neuro fuzzy , artificial intelligence , discriminant , fuzzy inference system , microwave , fuzzy logic , machine learning , engineering , electronic engineering , fuzzy control system , telecommunications
This article presents a new method based on adaptive neuro‐fuzzy inference system (ANFIS) to calculate the effective permittivities and characteristic impedances of microstrip lines. The ANFIS is a fuzzy inference system (FIS) implemented in the framework of an adaptive fuzzy neural network. It has the advantages of expert knowledge of FISs and learning capability of artificial neural networks. A hybrid learning algorithm, which combines the least square method and the back propagation algorithm, is used to identify the parameters of ANFIS. The results of ANFIS are compared with the results of the experimental works, quasi‐static methods, and a commercial electromagnetic simulator IE3D. There is very good agreement among the results of ANFIS models and quasi‐static methods, IE3D, and experimental works. © 2008 Wiley Periodicals, Inc. Microwave Opt Technol Lett 50: 1191–1196, 2008; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.23322