Neural networks modeling and parameterization applied to coplanar waveguide components

The present work describes the use of neural networks (NN) for multi-parame tric design and parameterization of coplanar waveguide (CPW) components. Th is technique allows one to reduce the CPU time required for intensive elect romagnetic (EM) simulations in a classical optimization procedure. Neural n etworks are used for modeling the high complex relationship between the phy sical parameters of a CPW circuit and its various frequency responses. In t his paper, the multi-layer perceptron neural network is used with one or tw o hidden layers due to its great capability for modeling complex structure behavior using data obtained from electromagnetic (EM) simulations. The val idity of the neural modeling is demonstrated by studying a CPW T-junction, Our proposed technique is applied on the modeling of a CPW low pass filter and a slot antenna fed by a CPW line, (C) 2000 John Wiley & Sons, Inc.