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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Journal ArticleDOI
Automated Deep Neural Learning-Based Optimization for High Performance High Power Amplifier Designs
TL;DR: This study presents an automated optimization-oriented strategy for designing high power amplifiers (HPAs) using deep neural networks (DNNs) that addresses the problem of heavy reliance of the system performance on the designer's experience and automatically generates valid layouts.
Proceedings ArticleDOI
Automated parametric modeling of microwave components using combined neural network and interpolation techniques
Weicong Na,Qi-Jun Zhang +1 more
TL;DR: An advanced algorithm for automated model generation (AMG) using neural networks is presented, to incorporate efficient interpolation approaches to make the AMG process much faster and to minimize the number of hidden neurons.
Journal ArticleDOI
Surrogate modeling of microwave structures using kriging, co-kriging, and space mapping
TL;DR: This paper considers various ways of enhancing SM surrogates by exploiting additional training data as well as two function approximation methodologies, kriging and co‐kriging, and presents a comprehensive numerical study in which they are compared.
Journal ArticleDOI
Neural Network Modeling for the Solution of the Inverse Loop Antenna Radiation Problem
TL;DR: Soft computing techniques are used to model and solve the inverse problem of a thin, circular, loop antenna that radiates in free space and the results predicted by the proposed models are in excellent agreement with the theoretical data obtained from the existing analytical solutions of the forward problem.
Journal ArticleDOI
Step on It Bringing Fullwave Finite-Element Microwave Filter Design up to Speed
L. Balewski,Grzegorz Fotyga,Michal Mrozowski,Martyna Mul,P. Sypek,Damian Szypulski,Adam Lamecki +6 more
TL;DR: There are many steps in the design of a microwave filter: mathematically describing the filter characteristics, representing the circuit as a network of lumped elements or as a coupling matrix, implementing the distributed elements, finding the initial dimensions of the physical structure, and carrying out numerical tuning using electromagnetic (EM) simulators.
References
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TL;DR: Thorough, well-organized, and completely up to date, this book examines all the important aspects of this emerging technology, including the learning process, back-propagation learning, radial-basis function networks, self-organizing systems, modular networks, temporal processing and neurodynamics, and VLSI implementation of neural networks.
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A Class of Methods for Solving Nonlinear Simultaneous Equations
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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.