Journal ArticleDOI
EM-based optimization of microwave circuits using artificial neural networks: the state-of-the-art
Reads0
Chats0
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
More filters
Journal ArticleDOI
Eye Diagram Contour Modeling Using Multilayer Perceptron Neural Networks With Adaptive Sampling and Feature Selection
TL;DR: This article presents a methodology for the modeling of high-speed systems using machine learning methods that is able to capture the shape and magnitude of the eye contours accurately, and the iterative nature of the algorithm allows a control to balance between accuracy and model generation time.
Journal ArticleDOI
Space mapping algorithm with improved convergence properties for microwave design optimization: Improved SM for Microwave Design Optimization
Slawomir Koziel,John W. Bandler +1 more
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 IssueDOI
Automated time domain modeling of linear and nonlinear microwave circuits using recurrent neural networks
Hitaish Sharma,Qi-Jun Zhang +1 more
TL;DR: An automated RNN modeling technique is proposed to efficiently determine the training waveform distribution and internal RNN structure during the offline training process, which extends a recent automatic model generation (AMG) algorithm from frequency-domain model generation to dynamic time- domain model generation.
Journal ArticleDOI
Time domain adjoint sensitivity analysis of electromagnetic problems with nonlinear media.
TL;DR: The proposed theory exploits the time-domain transmission line modeling (TLM) and provides an efficient AVM approach for sensitivity analysis of general time domain objective functions.
Journal ArticleDOI
Recent advances in parametric modeling of microwave components using combined neural network and transfer function
Feng Feng,Jianan Zhang,Jianan Zhang,Wei Zhang,Wei Zhang,Zhihao Zhao,Zhihao Zhao,Jing Jin,Jing Jin,Qi-Jun Zhang +9 more
TL;DR: An overview of recent advances in parametric modeling of microwave components using combined neural network and transfer function (neuro‐TF) and the sensitivity analysis‐based neuro‐TF modeling technique is provided.
References
More filters
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.