Journal ArticleDOI
EM-based optimization of microwave circuits using artificial neural networks: the state-of-the-art
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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Journal ArticleDOI
Shape-Preserving Response Prediction for Engineering Design Optimization
Slawomir Koziel,Leifur Leifsson +1 more
TL;DR: One of the most recent techniques of surrogate-based optimization techniques, the so-called shape-preserving response prediction (SPRP), is reviewed, discussing the formulation of SPRP, its limitations and generalizations, and its applications to solve design problems in various engineering areas.
Journal ArticleDOI
An Early History of Optimization Technology for Automated Design of Microwave Circuits
TL;DR: In this paper , the authors outline the early history of optimization technology for the design of microwave circuits, a personal journey filled with aspirations, academic contributions, and commercial innovations, which connects to today's multi-physics, system-level, and measurement-based optimization challenges exploiting confined and feature-based surrogates.
Modeling of rf mems switches for application in communication systems
TL;DR: An overview of properties, application areas and modeling of RF MEMS switches is presented, with a focus on the applications in the reconfigurable and adaptive mobile phones as well as in satellite communications.
EM-based design optimization of RF and microwave circuits using functional surrogate models
TL;DR: An effective CAD methodology to perform efficient EM-based design optimization of microwave circuits using surrogate models based on polynomial functional interpolants is described and exhaustive evaluation of the generalization performance of this surrogate modeling approach is addressed.
Proceedings ArticleDOI
A Novel Wide-Band Layout-level Synthesis Methodology for CMOS Integrated Spiral Inductors with Artificial Neural Network
TL;DR: In this paper, the authors developed a fast and accurate synthesis method to effectively generate CMOS spiral inductor's layout parameter using artificial neural network methodology, which can simulate and synthesize the geometric parameters of spiral inductors up to the frequency of 20 GHz.
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.