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
An Efficient Hybrid Sampling Method for Neural Network-Based Microwave Component Modeling and Optimization
TL;DR: The results show that the ANN model based on the proposed method achieves better modeling performance and yields better optimal design than the ANN models based on conventional sampling methods.
Journal ArticleDOI
Space Mapping Approach to Electromagnetic Centric Multiphysics Parametric Modeling of Microwave Components
Wei Zhang,Feng Feng,Venu-Madhav-Reddy Gongal-Reddy,Jianan Zhang,Shuxia Yan,Jianguo Ma,Qi-Jun Zhang +6 more
TL;DR: A novel technique to develop a low-cost electromagnetic (EM) centric multiphysics parametric model for microwave components using space mapping techniques to combine the computational efficiency of EM single physics (EM only) simulation with the accuracy of the multiph physics simulation.
Journal ArticleDOI
Bottom hole pressure estimation using hybridization neural networks and grey wolves optimization
TL;DR: New models for estimating bottom hole pressure of vertical wells with multiphase flow are proposed and the superiority of the hybridization ANN-GWO compared with the 2 other hybridizations or with the BP learning alone is demonstrated.
Journal ArticleDOI
A New Training Approach for Robust Recurrent Neural-Network Modeling of Nonlinear Circuits
Yi Cao,Qi-Jun Zhang +1 more
TL;DR: A new approach for developing recurrent neural-network models of nonlinear circuits is presented, overcoming the conventional limitations where training information depends on the shapes of circuit waveforms and/or circuit terminations.
Journal ArticleDOI
Gaussian Process Modeling of CPW-Fed Slot Antennas
J. P. de Villiers,J. P. Jacobs +1 more
TL;DR: Gaussian process (GP) regression is proposed as a structured supervised learning alternative to neural networks for the modeling of CPW-fed slot antenna input characteristics, with results of an accuracy comparable to the target moment-method-based full-wave simulations.
References
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Journal ArticleDOI
Optimal Global Rates of Convergence for Nonparametric Regression
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