Accurate radial wavelet neural-network model for efficient CAD modelling of microstrip discontinuities

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