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Neural Networks for...
Neural Networks for the Estimation of Low-Order Statistical Moments of a Stochastic Dielectric
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- Stenmark, Simon, 1988 (författare)
- Chalmers tekniska högskola,Chalmers University of Technology
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- Rylander, Thomas, 1972 (författare)
- Chalmers tekniska högskola,Chalmers University of Technology
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- McKelvey, Tomas, 1966 (författare)
- Chalmers tekniska högskola,Chalmers University of Technology
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(creator_code:org_t)
- 2021
- 2021
- Engelska.
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Ingår i: Conference Record - IEEE Instrumentation and Measurement Technology Conference. - 1091-5281. ; 2021-May
- Relaterad länk:
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- We present two different machine learning strategies to estimate the two lowest-order statistical moments of a two-dimensional inhomogeneous dielectric medium with stochastic variations, which have a Gaussian distribution for every point in the measurement region and a Gaussian auto-covariance function. In particular, we consider and compare (i) a fully-connected neural network and (ii) an affine model. These are trained to predict the pointwise mean and standard deviation of the underlying stochastic dielectric based on the scattering parameters, which are computed at the ports of four sensors that are placed around the circumference of the two-dimensional measurement region. We use the mean and variance of the real and imaginary part of the scattering parameters in a feature-extraction step before training. It is demonstrated that both machine learning strategies predict the mean permittivity well. However, the neural network outperforms the affine model for the prediction of the standard deviation. In addition, this article reviews the workflow for training, validating and testing a neural network in the context of measurement applications, where the ambition is to give an introduction to practitioners who would like to explore neural networks for their measurement application.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Bioinformatik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Bioinformatics (hsv//eng)
- NATURVETENSKAP -- Matematik -- Sannolikhetsteori och statistik (hsv//swe)
- NATURAL SCIENCES -- Mathematics -- Probability Theory and Statistics (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datorseende och robotik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Vision and Robotics (hsv//eng)
Nyckelord
- neural networks
- scattering parameters
- stochastic permittivity
- microwave measurement
- machine learning
- feature extraction
- hyperparameter tuning
Publikations- och innehållstyp
- kon (ämneskategori)
- ref (ämneskategori)
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