Neuromodeling of microwave circuits exploiting space-mapping technology

For the first time, we present modeling of microwave circuits using artific ial neural networks (ANN's) based on space-mapping (SM) technology. SM-base d neuromodels decrease the cost of training, improve generalization ability , and reduce the complexity of the ANN topology with respect to the classic al neuromodeling approach. Five creative techniques are proposed to generat e SM-based neuromodels, A frequency-sensitive neuromapping is applied to ov ercome the limitations of empirical models developed under quasi-static con ditions. Huber optimization is used to train the ANN's, We contrast SM-base d neuromodeling with the classical neuromodeling approach as well as with o ther state-of-the-art neuromodeling techniques, The SM-based neuromodeling techniques are illustrated by a microstrip bend and a high-temperature supe rconducting filter.