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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.

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Journal ArticleDOI

Artificial intelligence–based design optimization of nonuniform microstrip line band pass filter

TL;DR: A surrogate‐based model of a nonuniform microstrip transmission line (NTL) with a typical application of design optimization of a band‐pass filter for ISM band application using deep learning (DL) and meta‐heuristic optimization has been presented.
Journal ArticleDOI

Application of the NARX neural network as a digital predistortion technique for linearizing microwave power amplifiers

TL;DR: A digital predistortion (DPD) scheme to linearize power amplifiers (PAs) using a recurrent neural network called Nonlinear AutoRegressive with eXogenous input model (NARX) neural network (N ARXNN), based on a class of discrete‐time nonlinear system named NARX.
Journal ArticleDOI

Space Mapping-Have You Ever Wondered About the Engineer's Mysterious \"Feel\" for a Problem? [Speaker's Corner]

TL;DR: Chen's approach encapsulates the engineer's mysterious "feel" for a problem-an issue that had dogged my 30-year immersion in the art and science of optimization for computer-oriented engineering design as discussed by the authors.
Journal IssueDOI

Space mapping algorithm with improved convergence properties for microwave design optimization

TL;DR: This article investigates some techniques for improving the convergence properties of the space mapping algorithm, which are based on the general convergence results for such algorithms.
Journal ArticleDOI

Interpolated Coarse Models for Microwave Design Optimization With Space Mapping

TL;DR: An interpolation technique is used, which allows us to create coarse models that are both accurate and cheap, and overcomes the accuracy/cost dilemma described above, permitting significant reduction of the space-mapping optimization time.
References
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Book

Neural Networks: A Comprehensive Foundation

Simon Haykin
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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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.
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