Cognitive Broyden-based Input Space Mapping for Design Optimization

Abstract

Cognition-driven design of RF and microwave circuits is an emerging and promising approach to efficient design optimization of computationally expensive fine models. Existing techniques for cognition-driven design have been developed for optimizing microwave filters without exploiting traditional coarse model representations, e.g., equivalent circuits. Instead, intermediate feature-space parameters have been used to establish other types of mappings in the design process. In this paper, a cognitive space mapping (SM) technique that fully exploits traditional coarse models is proposed for the first time. The proposed cognitive SM approach exploits a previous cognition-driven parameter extraction (PE) formulation at each SM iteration. This cognitive SM technique follows an algorithmic structure that is an extension of that one used by the Broyden-based input SM, better known as aggressive space mapping (ASM). A synthetic benchmark example illustrates the performance improvement of the proposed cognitive SM versus ASM.