Journal ArticleDOI
EM-based optimization of microwave circuits using artificial neural networks: the state-of-the-art
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Abstract:
This paper reviews the current state-of-the-art in electromagnetic (EM)-based design and optimization of microwave circuits using artificial neural networks (ANNs). Measurement-based design of microwave circuits using ANNs is also reviewed. The conventional microwave neural optimization approach is surveyed, along with typical enhancing techniques, such as segmentation, decomposition, hierarchy, design of experiments, and clusterization. Innovative strategies for ANN-based design exploiting microwave knowledge are reviewed, including neural space-mapping methods. The problem of developing synthesis neural networks is treated. EM-based statistical analysis and yield optimization using neural networks is reviewed. The key issues in transient EM-based design using neural networks are summarized. The use of ANNs to speed up "global modeling" for EM-based design of monolithic microwave integrated circuits is briefly described. Future directions in ANN techniques to microwave design are suggested.read more
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Journal ArticleDOI
Recent advances in neural network‐based inverse modeling techniques for microwave applications
TL;DR: An overview of recent advances in neural network‐based inverse modeling techniques for microwave applications and the recently proposed activation function and three‐stage deep learning algorithm for training the hybrid deep neural network are reviewed.
Journal ArticleDOI
EM-Centric Multiphysics Optimization of Microwave Components Using Parallel Computational Approach
TL;DR: The proposed MPO technique takes a small number of iterations to obtain the optimal EM-centric MP response and can be valid in a relatively large neighborhood, which makes an effective and large optimization update in each optimization iteration.
Journal ArticleDOI
Surrogate modelling and optimization using shape-preserving response prediction: A review
Leifur Leifsson,Slawomir Koziel +1 more
TL;DR: This article reviews a particular technique of this type, namely, shape-preserving response prediction (SPRP), which works on the level of the model responses to correct the underlying low-fidelity models.
Journal ArticleDOI
Space Mapping Technique Using Decomposed Mappings for GaN HEMT Modeling
TL;DR: A novel space mapping (SM) modeling approach for gallium nitride (GaN) high-electron-mobility transistors (HEMTs) with trapping effects is presented in this article, advancing the SM technique for nonlinear device modeling.
Journal IssueDOI
Neural input space mapping optimization based on nonlinear two-layer perceptrons with optimized nonlinearity
TL;DR: This work is an improved version of the Neural Space-Mapping algorithm that uses three layer perceptrons (3LP) to implement a nonlinear input mapping function at each iteration, whose nonlinearity is automatically regulated with classical optimization algorithms.
References
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Journal ArticleDOI
Optimal Global Rates of Convergence for Nonparametric Regression
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