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

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. © 2015 Wiley Periodicals, Inc. Int J RF and Microwave CAE 25:563-572, 2015.