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
Proceedings ArticleDOI
Recent Advances in Deep Neural Network Technique for High-Dimensional Microwave Modeling
TL;DR: In this article, a hybrid deep neural network that employs both the sigmoid function and the smooth rectified linear unit (ReLU) as activation functions is used for microwave modeling in order to address the challenges due to high-dimensional inputs.
Journal ArticleDOI
Applications of Artificial Neural Network Techniques in Microwave Filter Modeling, Optimization and Design
TL;DR: This paper reviews state-of-the-art microwave fllter modeling, optimization and design methods using artiflcial neural network (ANN) technique and shows that ANN can provide accurate design parameters and after learning phase the computational cost is lower than the one associated with full wave model analysis.
Proceedings ArticleDOI
Developing 3-step modeling strategy exploiting knowledge based techniques
TL;DR: 3-step modeling strategy based on knowledge based techniques is proposed to develop new efficient modeling instead of conventional artificial neural network (ANN) modeling to improve modeling accuracy but also to reduce time consumption during modeling.
Journal ArticleDOI
Ensemble-based surrogate modeling of microwave antennas using XGBoost algorithm
TL;DR: XGBoosting‐based ensemble learning had been used for having surrogate models for three different microwave designs and had achieved a remarkable performance both based on its own performance measures and its comparison with the counterpart algorithms.
Journal ArticleDOI
Adaptive CAD-Model Construction Schemes
TL;DR: Two advanced surrogate model construction techniques are discussed and the quality of RBF models is satisfactory, the performance of the ANN models obtained with a new training scheme is superior and comparable to the rational function models.
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.