Knowledge-Based Artificial Neural Network Models for Microstrip Radial Stubs

Abstract

Radial stubs are a superior choice over low characteristic impedance rectangular stubs in terms of providing an accurate localized zero-impedance reference point and maintaining a low input impedance value over a wide frequency range. In this paper, knowledge-based artificial neural networks are used to model the microstrip radial stubs. Using space-mapping technology and Huber optimization make the neural network models for radial stubs decrease the number of training data, improve generalization ability, and reduce the complexity of the neural network topology with respect to the classical neuromodeling approach. The neural networks are developed for design and optimization of radial stubs, which are robust both from the angle of time of computation and accuracy.