ANN-based modeling of compact impedance-varying transmission lines with applications to ultra-wideband Wilkinson power dividers

Alshamaileh, KA, Almalkawi, MJ, Junuthula, RR, Devabhaktuni, VK and Aaen, PH (2015) ANN-based modeling of compact impedance-varying transmission lines with applications to ultra-wideband Wilkinson power dividers International Journal of RF and Microwave Computer-Aided Engineering, 25 (7). pp. 563-572.

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Abstract

© 2015 Wiley Periodicals, Inc.The modeling of the physical and electrical characteristics of microstrip non-uniform transmission lines (NTLs) utilizing artificial neural networks (ANNs) is investigated. The fundamental equations and constraints for designing variable impedance transmission lines are first presented. Then, a proof-of-concept example of a compact non-uniform matching transformer and the counterpart modeled version is elaborated for source and load impedances Zs and Zl, respectively, at 0.5 GHz. For comparison purposes, weights and biases of the proposed ANN are established with three different training techniques; namely: backpropagation (BP), Quasi-Newton (QN), and conjugate gradient (CG); at which the ABCD matrix, impedance variations, input port matching (S11), and transmission parameter (S21) are set as benchmarks to examine the validity of the trained model. The concept is then extended to model a NTL ultrawideband (UWB) Wilkinson power divider (WPD) with three resistors for improved isolation. S-parameters derived from the trained ANN outputs are close to those obtained by the traditional time-consuming optimization procedure, and show input and output ports matching and isolation of below -10 dB, and acceptable values of transmission parameters over the 3.1 GHz to 10.6 GHz band. The resulting models outperform traditional optimizations in terms of simulation time and reserved resources with comparable accuracy.

Item Type:	Article
Authors :	Name Email ORCID Alshamaileh, KA Almalkawi, MJ Junuthula, RR Devabhaktuni, VK Aaen, PH
Date :	1 January 2015
Identification Number :	10.1002/mmce.20893
Depositing User :	Symplectic Elements
Date Deposited :	28 Mar 2017 15:54
Last Modified :	31 Oct 2017 18:28
URI:	http://epubs.surrey.ac.uk/id/eprint/811288

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