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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

Recent Advances and Future Trends in Neuro-Tffor EM Optimization

TL;DR: In this article , an advanced knowledge-based ANN technique, called neuro-transfer function (short for neuro-TF), has been developed, which is used as the prior knowledge that expresses the highly nonlinear EM responses versus frequency.

Extreme Learning Machine with a Modified Flower Pollination Algorithm for Filter Design

TL;DR: In this paper, a modified flower pollination algorithm based on the steepest descent method (SDM) is proposed to set the optimal initial weights and thresholds of the extreme learning machine (ELM) for microwave filter design.
Journal ArticleDOI

Fully Automated Design Method Based on Reinforcement Learning and Surrogate Modeling for Antenna Array Decoupling

TL;DR: In this article , a machine learning framework was proposed to solve the problem of automated design for EM tasks, which combines advanced reinforcement learning (RL) algorithms and deep neural networks (DNNs) to simulate the decision-making process of human designers.
Proceedings ArticleDOI

Analysis of Training Data Sets in Artificial Neural Networks Applied to a Radio Frequency Problem

TL;DR: In this article, a novel study has been carried out to understand the functionality of artificial neural network (ANN) algorithms on a radio frequency (RF) problem, which determined the dominant TM 0 resonant frequency of a rectangular microstrip patch located above dielectric substrate backed by a ground plane.
Proceedings ArticleDOI

Recent advances in EM modeling and optimization exploiting parallel Computations

TL;DR: The most recent techniques are discussed in this paper including the advanced pole-residue-based neuro-transfer function (neuro-TF) modeling technique and the parallel gradient based EM optimization technique.
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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