Response clustering for electromagnetic modeling and optimization

Developing models from computational data is a major focus in electromagnetic design. This paper introduces ways of creating customized neural models based on a fuzzy clustering of responses. Fuzzy-clustered neural network (FCNN) models are explored, leading to increases in accuracy. The information contained within FCNN models can also be applied to space mapping electromagnetic optimization. This optimization approach strives to combine the accuracy of fine models (such as finite elements) with the low cost of coarse models. These FCNN enhancements are demonstrated through a patch antenna test case. Index Terms--Artificial neural networks (ANNs), electromagnetic modeling, electromagnetic optimization, fuzzy clustering.