Artificial neural networks for RF and microwave design - from theory to practice

@article{citeulike:2801965, abstract = {Neural-network computational modules have recently gained recognition as an unconventional and useful tool for RF and microwave modeling and design. Neural networks can be trained to learn the behavior of passive/active components/circuits. A trained neural network can be used for high-level design, providing fast and accurate answers to the task it has learned. Neural networks are attractive alternatives to conventional methods such as numerical modeling methods, which could be computationally expensive, or analytical methods which could be difficult to obtain for new devices, or empirical modeling solutions whose range and accuracy may be limited. This tutorial describes fundamental concepts in this emerging area aimed at teaching RF/microwave engineers what neural networks are, why they are useful, when they can be used, and how to use them. Neural-network structures and their training methods are described from the RF/microwave designer's perspective. Electromagnetics-based training for passive component models and physics-based training for active device models are illustrated. Circuit design and yield optimization using passive/active neural models are also presented. A multimedia slide presentation along with narrative audio clips is included in the electronic version of this paper. A hyperlink to the NeuroModeler demonstration software is provided to allow readers practice neural-network-based design concepts.}, author = {Zhang, Qi-Jun and Gupta, K. C. and Devabhaktuni, V. K. }, booktitle = {Microwave Theory and Techniques, IEEE Transactions on}, citeulike-article-id = {2801965}, doi = {10.1109/TMTT.2003.809179}, journal = {Microwave Theory and Techniques, IEEE Transactions on}, keywords = {design, microwave, network, neural, rf}, number = {4}, pages = {1339--1350}, posted-at = {2008-05-15 16:11:25}, priority = {2}, title = {Artificial neural networks for RF and microwave design - from theory to practice}, url = {http://dx.doi.org/10.1109/TMTT.2003.809179}, volume = {51}, year = {2003} }

TY - JOUR ID - citeulike:2801965 L3 - citeulike-article-id:2801965 TI - Artificial neural networks for RF and microwave design - from theory to practice T2 - Microwave Theory and Techniques, IEEE Transactions on JF - Microwave Theory and Techniques, IEEE Transactions on VL - 51 IS - 4 SP - 1339 EP - 1350 N2 - Neural-network computational modules have recently gained recognition as an unconventional and useful tool for RF and microwave modeling and design. Neural networks can be trained to learn the behavior of passive/active components/circuits. A trained neural network can be used for high-level design, providing fast and accurate answers to the task it has learned. Neural networks are attractive alternatives to conventional methods such as numerical modeling methods, which could be computationally expensive, or analytical methods which could be difficult to obtain for new devices, or empirical modeling solutions whose range and accuracy may be limited. This tutorial describes fundamental concepts in this emerging area aimed at teaching RF/microwave engineers what neural networks are, why they are useful, when they can be used, and how to use them. Neural-network structures and their training methods are described from the RF/microwave designer's perspective. Electromagnetics-based training for passive component models and physics-based training for active device models are illustrated. Circuit design and yield optimization using passive/active neural models are also presented. A multimedia slide presentation along with narrative audio clips is included in the electronic version of this paper. A hyperlink to the NeuroModeler demonstration software is provided to allow readers practice neural-network-based design concepts. KW - design KW - microwave KW - network KW - neural KW - rf AU - Zhang, Qi-Jun AU - Gupta, KC AU - Devabhaktuni, VK PY - 2003/// UR - http://dx.doi.org/10.1109/TMTT.2003.809179 ER -