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An accurate wavelet neural‐network‐based model for electromagnetic optimization of microwave circuits
Author(s) -
Bila S.,
Harkouss Y.,
Ibrahim M.,
Rousset J.,
N'Goya E.,
Baillargeat D.,
Verdeyme S.,
Aubourg M.,
Guillon P.
Publication year - 1999
Publication title -
international journal of rf and microwave computer‐aided engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.335
H-Index - 39
eISSN - 1099-047X
pISSN - 1096-4290
DOI - 10.1002/(sici)1099-047x(199905)9:3<297::aid-mmce13>3.0.co;2-w
Subject(s) - initialization , broyden–fletcher–goldfarb–shanno algorithm , artificial neural network , computer science , wavelet , convergence (economics) , finite element method , microwave , algorithm , electronic circuit , electronic engineering , artificial intelligence , engineering , electrical engineering , telecommunications , computer network , asynchronous communication , structural engineering , economics , programming language , economic growth
In this paper, a relevant automated electromagnetic (EM) optimization method and a novel, fast, and accurate artificial neural network are proposed for the efficient CAD modeling of microwave circuits. We lay the groundwork for our investigation of radial wavelet neural networks WNNs trained by BFGS (Broyden‐Fletcher‐Goldfarb‐Shanno) and LBFGS (limited memory BFGS) algorithms and their application to determine the scattering parameters of the circuit under study. Wavelet theory may be exploited in deriving a good initialization for the neural network, and thus improved convergence of the learning algorithm. The optimization method combines a rigorous and accurate global EM analysis of device performed with a finite‐element method (FEM) and a fast neural model deduced from its segmented EM analysis. Finally, experimental results, which confirm the validity of the WNN model, and good agreement between theoretical optimization results and experimental ones are reported. ©1999 John Wiley & Sons, Inc. Int J RF and Microwave CAE 9: 297–306, 1999.