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
New neural method for bias dependent noise modelling of microwave transistors
TL;DR: A new method for accurate determination of noise parameters of microwave transistors for various bias conditions is proposed, consisting of a transistor empirical noise model (modification of Pospieszalski’s noise model) and two artificial neural networks.
Proceedings ArticleDOI
Uniaxial discontinuities analysis using the artificial neural networks
TL;DR: A novel computer-aided design (CAD) tool based on the artificial neural networks to the analysis of uniaxial discontinuities in rectangular waveguides is proposed.
Proceedings ArticleDOI
Application of neural networks for linear/nonlinear microwave modeling
Lei Zhang,Kui Bo,Qi-Jun Zhang +2 more
TL;DR: ANN based models can automatically learn the microwave component or circuit behaviors with satisfactory accuracy, and the trained ANN models are able to implement into commercial circuit simulators for efficient design and optimization.
Book ChapterDOI
Shape-Preserving Response Prediction for Surrogate Modeling and Engineering Design Optimization
Slawomir Koziel,Leifur Leifsson +1 more
TL;DR: This chapter reviews one of the most recent SBO techniques, the so-called shape-preserving response prediction (SPRP), and discusses the formulation of SPRP, its limitations, and generalizations, and demonstrates its applications to solve design problems in various engineering areas, including microwave engineering, antenna design, and aerodynamic shape optimization.
Proceedings ArticleDOI
A Metasurface Modeling Method Based on Generative Adversarial Network Combined with K-Nearest Neighbor
TL;DR: In this paper , a machine learning-based metasurface modeling method using generative adversarial network (GAN) and k-nearest neighbor (k-NN) is proposed.
References
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Book
Neural Networks: A Comprehensive Foundation
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.
Journal ArticleDOI
Backpropagation through time: what it does and how to do it
TL;DR: This paper first reviews basic backpropagation, a simple method which is now being widely used in areas like pattern recognition and fault diagnosis, and describes further extensions of this method, to deal with systems other than neural networks, systems involving simultaneous equations or true recurrent networks, and other practical issues which arise with this method.
Journal ArticleDOI
A Class of Methods for Solving Nonlinear Simultaneous Equations
TL;DR: In this article, the authors discuss certain modifications to Newton's method designed to reduce the number of function evaluations required during the iterative solution process of an iterative problem solving problem, such that the most efficient process will be that which requires the smallest number of functions evaluations.
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.