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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Book ChapterDOI
Efficient Reconfigurable Microstrip Patch Antenna Modeling Exploiting Knowledge Based Artificial Neural Networks
Murat Simsek,Ashrf Aoad +1 more
TL;DR: Simulation results show that knowledge based neural networks ensure considerable savings in computational costs as compared to the computationally intensive 3D-EM simulation while maintaining the accuracy of the fine model.
Journal ArticleDOI
Efficient FEM-Based EM Optimization Technique Using Combined Lagrangian Method With Newton’s Method
TL;DR: The proposed EM optimization using the combined Lagrangian method with Newton’s method can converge faster than direct EM optimizations with other gradient-based optimization methods.
Journal ArticleDOI
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TL;DR: In this article, an artificial neural network-based behavioral model of varactors fabricated in advanced FinFET technology is proposed and verified by comparing measured and simulated scattering parameters up to 50GHz.
Proceedings ArticleDOI
ANN based inverse modeling of RF MEMS capacitive switches
Zlatica Marinkovic,Tomislav Ciric,Teayoung Kim,Larissa Vietzorreck,Olivera Pronic-Rancic,Marija Milijic,Vera Markovic +6 more
TL;DR: The development of ANN based procedures to be used as a feed-forward tool for determination of the switch geometrical parameters avoiding optimizations is proposed and a procedure is developed to achieve the desired electrical resonance frequency or the necessary actuation voltage.
Proceedings ArticleDOI
ANN and space mapping for microwave modelling and optimization
TL;DR: An overview of the state-of-art of microwave modelling and design with ANN, space mapping and neuro-space mapping is presented.
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.