Artificial neural networks in their simplest forms for analysis and synthesis of RF/microwave planar transmission lines

Abstract

In this work, the multiplayer perceptron (MLP) and radial basis function (RBF) neural networks in their simplest forms are employed in function approximation for highly nonlinear and complex analysis and synthesis of the most commonly used planar RF/microwave transmission lines, that is, microstrip lines, coupled microstrip lines, and basic and shielded coplanar-waveguides. Since the analysis and synthesis processes for these systems have one-to-one mapping relations with each other, a forward model is defined for the analysis process for all these types of the planar transmission lines; on the other hand, a reverse model is also considered for the synthesis of the same lines. This reverse model is realized by swapping some of the inputs/outputs in the analysis model, and training the neural networks accordingly. Both MLP and RBF types of neural models are applied to the four widely used anisotropic and isotropic dielectric materials: PTFE/microstrip glass, RT/Duroid 6006, alumina and gallium arsenide. The results are shown to agree very well with the targets. A low-pass filter with 30-dB attenuation frequency at 3.5 GHz on an alumina substrate is designed by the use of a neural-network synthesis and its resulting performance agrees well with the one using analytical formulas for the synthesis. (c) 2005 Wiley Periodicals. Inc.