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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Journal ArticleDOI
Synthesis of Integrated Passive Components for High-Frequency RF ICs Based on Evolutionary Computation and Machine Learning Techniques
TL;DR: Compared with available methods with the best solution quality, MMLDE can obtain comparable results, and has approximately a tenfold improvement in computational efficiency, which makes the computational time for optimized component synthesis acceptable.
Journal ArticleDOI
Compact microstrip dual-band bandpass filters design using genetic-algorithm techniques
Ming-Iu Lai,Shyh-Kang Jeng +1 more
TL;DR: In this paper, a hybrid-coded genetic-algorithm (GA) based approach is proposed to design compact dual-band bandpass filters with microstrip lines, and two examples are designed and implemented to validate the proposed algorithm.
Journal ArticleDOI
Artificial Neural Networks Based Optimization Techniques: A Review
Maher G. M. Abdolrasol,Sajid Hussain,Taha Selim Ustun,Mahidur R. Sarker,Mahammad A. Hannan,Ramizi Mohamed,Jamal Abd Ali,Saad Mekhilef,Abdalrhman Milad +8 more
TL;DR: In this article, an extensive review of artificial neural networks (ANNs) based optimization algorithm techniques with some of the famous optimization techniques, e.g., genetic algorithm (GA), particle swarm optimization (PSO), artificial bee colony (ABC), and backtracking search algorithm (BSA), is presented.
Journal ArticleDOI
Parallel Space-Mapping Approach to EM Optimization
TL;DR: The proposed formulation of multi-point surrogate model training is inherently suited to and implemented through parallel computation and includes multiple fine model evaluation in parallel and multi- point surrogate training using a parallel algorithm.
Journal ArticleDOI
Bayesian Optimization for Broadband High-Efficiency Power Amplifier Designs
TL;DR: This paper proposes a novel, optimization-oriented strategy for the design of broadband, high-efficiency power amplifiers (PAs) using Bayesian optimization (BO), which outperforms a commercial electronic design automation (EDA) software's built-in optimizer, demonstrating that the EM-based BO is well-suited to the challenge of high power designs.
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.