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Comparison of adaptive-network-based fuzzy inference system models for resonant frequency computation of circular microstrip antennas

  • Electrodynamics and Wave Propagation
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Abstract

This paper presents a method based on adaptive-network-based fuzzy inference system (ANFIS) to compute the resonant frequency of a circular microstrip antenna (MSA). The ANFIS is a class of adaptive networks which are functionally equivalent to fuzzy inference systems (FISs). Seven optimization algorithms, least-squares, nelder-mead, differential evolution, genetic, hybrid learning, particle swarm, and simulated annealing, are used to determine optimally the design parameters of the ANFIS. The results of the ANFIS models show better agreement with the experimental results, as compared to the results of previous methods available in the literature. When the performances of ANFIS models are compared with each other, the best result is obtained from the ANFIS model trained by the least-squares algorithm.

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Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, Faculty of Engineering, Erciyes University, 38039, Kayseri, Turkey

    K. Guney

  2. Department of Aircraft Electrical and Electronics, Civil Aviation School, Erciyes University, 38039, Kayseri, Turkey

    N. Sarikaya

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  1. K. Guney
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  2. N. Sarikaya
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Correspondence to K. Guney.

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Published in Russian in Radiotekhnika i Elektronika, 2009, Vol. 54, No. 4, pp. 389–400.

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Guney, K., Sarikaya, N. Comparison of adaptive-network-based fuzzy inference system models for resonant frequency computation of circular microstrip antennas. J. Commun. Technol. Electron. 54, 369–380 (2009). https://doi.org/10.1134/S1064226909040019

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