Regression with Deep Learning for Sensor Performance Optimization
Signal Processing
2021-03-30 v2 Machine Learning
Machine Learning
Abstract
Neural networks with at least two hidden layers are called deep networks. Recent developments in AI and computer programming in general has led to development of tools such as Tensorflow, Keras, NumPy etc. making it easier to model and draw conclusions from data. In this work we re-approach non-linear regression with deep learning enabled by Keras and Tensorflow. In particular, we use deep learning to parametrize a non-linear multivariate relationship between inputs and outputs of an industrial sensor with an intent to optimize the sensor performance based on selected key metrics.
Keywords
Cite
@article{arxiv.2002.11044,
title = {Regression with Deep Learning for Sensor Performance Optimization},
author = {Ruthvik Vaila and Denver Lloyd and Kevin Tetz},
journal= {arXiv preprint arXiv:2002.11044},
year = {2021}
}
Comments
Accepted in Workshop on Microelectronics and Electron Devices March 30th, 2020