Knowledge-based artificial neural network models for microstrip radial stubs

Radial stubs are a superior choice over low characteristic impedance rectan gular stubs in terms of providing an accurate localized zero-impedance refe rence point and maintaining a low input impedance value over a wide frequen cy range. In this paper, knowledge-based artificial neural networks are use d 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 th e complexity of the neural network topology with respect to the classical n euromodeling approach. The neural networks are developed for design and opt imization of radial stubs, which are robust both from the angle of time of computation and accuracy.