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
Time-Domain Neural Network Approaches to EM Modeling of Microwave Components
Qi-Jun Zhang,Yi Cao +1 more
TL;DR: Through two examples it’s demonstrated that the trained time domain neural network model provides fast EM solutions with variable values of the geometrical parameter in the model.
Proceedings ArticleDOI
Recent Advances in Parametric Modeling Using Pole-Residue-Based Neuro-Transfer Functions
TL;DR: An overview of the advance in parametric modeling using combined neural networks and pole-residue-based transfer functions is provided to speedup model development by reducing the number of training data required for developing the model.
Dissertation
Contribución al desarrollo de técnicas CAD para el diseño de antenas impresas y dispositivos pasivos de microondas basadas en el método de elementos finitos
TL;DR: In this paper, the authors propose an approach for the desarrollo of herramientas numericas basadas en tecnicas de onda completa for the diseno asistido by ordenador (Computer-Aided Design, ‘CAD’) of dispositivos de microondas.
Journal ArticleDOI
Bias-dependent hybrid PKI empirical–neural model of microwave FETs
TL;DR: In this article, a hybrid empirical-neural model of microwave field effect transistors is proposed, which is a combination of an equivalent circuit model including noise developed for one bias point and two prior knowledge input artificial neural networks (PKI ANNs).
Proceedings ArticleDOI
A 5G Multibeam Antenna Including Rotman Lens and Slot Array Antenna
TL;DR: In this paper, a multibeam network is designed and simulated in 28 GHz which consists of a Rotman lens and a planar slot array antenna, which is implemented on a Substrate Integrated Waveguide (SIW).
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.
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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.