Accurate radial wavelet neuralnetwork model for efficient CAD modelling of microstrip discontinuities

Abstract : In the paper, a novel, fast and accurate artificial neural network is proposed for efficient computer-aided design (CAD) modelling of microstrip discontinuities. The authors lay the groundwork for their investigation of radial-wavelet neural networks RWNN and their application, to determine the scattering parameters of the circuit under study. Wavelet theory may be exploited in deriving a good initialisation for the neural network, and thus improved convergence of the learning algorithm. The problem of finding a good model is then discussed through solutions offered by radial-wavelet networks trained by Broyden-Fletcher-Goldfarb-Shanno (BFGS) and limited memory BFGS (LBFGS) algorithms. Finally, experimental results, which confirm the validity of the RWNN model, are reported
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https://hal-unilim.archives-ouvertes.fr/hal-00924419
Contributeur : Edouard Ngoya <>
Soumis le : lundi 6 janvier 2014 - 17:29:57
Dernière modification le : jeudi 11 janvier 2018 - 06:17:28

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Youcef Harkouss, Edouard Ngoya, Jean Rousset, Daniel Argollo. Accurate radial wavelet neuralnetwork model for efficient CAD modelling of microstrip discontinuities. Microwaves, Antennas and Propagation, IEE Proceedings, 2000, 17 (4), pp.277 - 283. ⟨10.1049/ip-map:20000576⟩. ⟨hal-00924419⟩

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