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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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Proceedings ArticleDOI

A new generalized state-space dynamic neural network method for I/O buffer modeling in high-speed PCB design

Yi Cao, +1 more
TL;DR: It is demonstrated that the proposed method provides better accuracy compared to the existing SSDNN for modeling I/O buffers with strong nonlinearity and a long propagation delay, while outperforming the detailed SPICE model in terms of simulation efficiency.
Proceedings ArticleDOI

Machine Learning Technologies for RF/Microwave CAD: Past, Present and Future Perspectives

TL;DR: Machine learning for microwave computer-aided design (CAD) started in the 1990s in the form of Artificial Neural Networks (ANN) for RF/Microwave design as discussed by the authors .
Proceedings ArticleDOI

Improved ANN approach to bias-dependent scalable noise modelling of microwave FETs

TL;DR: The prior knowledge neural approach can be successfully applied for bias dependent noise modelling of microwave transistors and is extended to a class of devices made in the same technology but differing in the gate width.
Journal ArticleDOI

Machine Learning-Based Generative Optimization Method and Its Application to Antenna Decoupling Design

TL;DR: In this paper , a machine learning-based generative optimization method using Masked Autoencoders (MAE) is proposed and applied to multi-objective antenna decoupling structure design.
Journal ArticleDOI

Yapay Sinir Ağları İle Çeyrek Daire Yarıklı Mikroşerit Yama Antenin Rezonans Frekansının Belirlenmesi

TL;DR: Geliştirilen yeni mikroşerit anten geometrilerinde çalışmada çeyrek daire yarıklı mikroserit antennae ait farklı giriş verileri (anten fiziksel parametreleri) için rezonans frekansını elde edilmesini sağlayan çok katmanlı YSA modeli oluşturulmuştur as mentioned in this paper .
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.
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.
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