Recent advances in knowledge‐based model structure optimization and extrapolation techniques for microwave applications. (14th March 2021)

• Record Type: Journal Article
• Authors: Na, Weicong; Yan, Shuxia; Feng, Feng; Liu, Wenyuan; Zhu, Lin; Zhang, Qi‐Jun
• Other Names: Vadalà Valeria guestEditor.; Crupi Giovanni guestEditor.
• Is Part Of: International journal of numerical modelling. Volume 34:Number 5(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-03-14
• Subjects: extrapolation -- knowledge‐based neural network -- microwave modeling -- model structure optimization; Electric networks -- Mathematical models -- Periodicals; Electronics -- Mathematical models -- Periodicals; 621.3011
• DOI: 10.1002/jnm.2879
• Languages: English
• ISSNs: 0894-3370

Abstract

Abstract: Artificial neural network modeling techniques have been recognized as important vehicles in the microwave computer‐aided design (CAD) area in addressing the growing challenges of designing next generation microwave device, circuits, and systems. This article provides an overview of recent advances in knowledge‐based neural network model generation and extrapolation techniques for microwave applications. We first introduce the unified knowledge‐based neural network structure optimization technique. Using the distinctive property for feature selection of l 1 optimization, this unified modeling technique efficiently determines the type and topology of the mapping structure in a knowledge‐based model. This knowledge‐based model structure optimization technique is more flexible and systematic, and can further speed up the knowledge‐based neural model development. As a further advancement, we also discuss the advanced multi‐dimensional extrapolation technique for neural‐based microwave modeling. The purpose is to make the neural network model can be reliably used not only inside the training range but also outside the training range. Multi‐dimensional cubic polynomial extrapolation formulation and optimization over grids outside the training range are utilized to make neural models more robust and reliable when they are used outside the training range. The validity of these techniques is demonstrated by microwave modeling examples.