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
Citations
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Diseño de circuitos de alta frecuencia usando mapeo espacial neural con no linealidad regulada
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Book ChapterDOI
Knowledge-Based Variable-Fidelity Optimization of Expensive Objective Functions through Space Mapping
Slawomir Koziel,John W. Bandler +1 more
TL;DR: This work presents and formulate the original input space mapping concept, as well as the more recent implicit and output space mapping concepts, and illustrates both input and implicit space mapping through the space mapping optimization of a simple, technology-free wedgecutting problem.
Journal ArticleDOI
Development and validation of ANN approach for extraction of MESFET/HEMT noise model parameters
TL;DR: In this paper, an efficient neural approach for straightforward determination of the noise model parameters, avoiding optimizations, is proposed, and a detailed validation of the proposed approach was done by comparison of the measured transistor noise parameters with those obtained by using the extracted noises model parameters for two noise models, the Pospieszalski's noise model and the noise wave model.
Journal ArticleDOI
Adversarial-Network Regularized Inverse Design of Frequency-Selective Surface With Frequency-Temporal Deep Learning
TL;DR: In this article , an adversarial-network regularized inverse-design scheme with frequency-temporal deep learning method (AR-FTDL) was proposed to solve the non-uniqueness in mapping from the demand space to geometry space and the difficulty in incorporation of fabrication constraints severely hinder the practical applications of these data-driven methods.
Journal ArticleDOI
Systematic Order Fitting Algorithm in Neuro-TF for Parametric Modeling of Microwave Components
TL;DR: In this paper , the authors proposed a novel systematic order fitting technique for parametric modeling using a combined neural network and transfer function (neuro-TF) technique for electromagnetic (EM) behaviors of microwave components.
References
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Journal ArticleDOI
A Class of Methods for Solving Nonlinear Simultaneous Equations
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Journal ArticleDOI
Optimal Global Rates of Convergence for Nonparametric Regression
TL;DR: In this article, it was shown that the optimal rate of convergence for an estimator of an unknown regression function (i.e., a regression function of order 2p + d) with respect to a training sample of size n = (p - m)/(2p + 2p+d) is O(n−1/n−r) under appropriate regularity conditions, where n−1 is the optimal convergence rate if q < q < \infty.