Space Mapping Based NeuromodelingArtificial Neural Networks (ANN) are very convenient in modeling highdimensional and highly nonlinear components, as those found in the microwave and high frequency arena, due to their ability to learn and generalize from data, their nonlinear processing nature, and their massively parallel structure. In modeling high frequency components the learning data is usually obtained from a detailed or "fine" model (EM simulator or measurements). This is generally very time consuming because the simulation/measurements must be performed for many combinations of different values of input parameters. This is the main drawback of classical ANN modeling. Without sufficient learning samples, the neural models may not be reliable. We developed a powerful new concept in neuromodeling of microwave circuits based on Space Mapping technology. By taking advantage of the vast set of empirical or "coarse" models already available, Space Mapping based neuromodels decrease the number of EM simulations for training, improve generalization ability and reduce the complexity of the ANN topology with respect to the classical neuromodeling approach.
References J.W. Bandler, M.A. Ismail, J.E. RayasSánchez and Q. J. Zhang, "Neuromodeling of microwave circuits exploiting space mapping technology," IEEE Trans. Microwave Theory Tech., vol. 47, 1999. J.W. Bandler, J.E. RayasSánchez and Q.J. Zhang, "Neural modeling and space mapping: two approaches to circuit design," (invited), XXVI URSI General Assembley (Toronto, ON), 1999. J.W. Bandler, M.A. Ismail, J.E. RayasSánchez and Q. J. Zhang, "New directions in model development for RF/microwave components utilizing artificial neural networks and space mapping," (invited), IEEE APS Int. Symp. (Orlando, FL), 1999. J.W. Bandler, M.A. Ismail, J.E. RayasSánchez and Q. J. Zhang, "Neuromodeling of microwave circuits exploiting space mapping technology," IEEE MTTS Int. Microwave Symp. Digest (Anaheim, CA), 1999, pp. 149152. J.W. Bandler, J.E. RayasSánchez and Q. J. Zhang,
"Space mapping based neuromodeling of highfrequency circuits," Micronet Annual
Workshop (Ottawa, ON), 1999, pp. 122123.
