Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Aniruddha Rajendra Rao"'
Autor:
Jana Backhus, Aniruddha Rajendra Rao, Chandrasekar Venkatraman, Abhishek Padmanabhan, A. Vinoth Kumar, Chetan Gupta
Publikováno v:
Applied Sciences, Vol 14, Iss 8, p 3270 (2024)
In this study, we leverage SCADA data from diverse wind turbines to predict power output, employing advanced time series methods, specifically Functional Neural Networks (FNN) and Long Short-Term Memory (LSTM) networks. A key innovation lies in the e
Externí odkaz:
https://doaj.org/article/5d6ba42a6ce44325b290f9e45bceff52
We introduce a new class of non-linear models for functional data based on neural networks. Deep learning has been very successful in non-linear modeling, but there has been little work done in the functional data setting. We propose two variations o
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9e38b66a82f37cccb60687f557919765
Publikováno v:
IEEE BigData
In the last few decades, building regression models for non-scalar variables, including time series, text, image, and video, has attracted increasing interests of researchers from the data analytic community. In this paper, we focus on a multivariate
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::be926be508092a3f1d638188b6aa666a
https://doi.org/10.1109/bigdata50022.2020.9378087
https://doi.org/10.1109/bigdata50022.2020.9378087
Publikováno v:
DSAA
Explosive growth in spatio-temporal data and its wide range of applications have attracted increasing interests of researchers in the statistical and machine learning fields. The spatio-temporal regression problem is of paramount importance from both
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bc094cd67ff6b5ed1b04ab4358a106c7
https://doi.org/10.1109/dsaa49011.2020.00020
https://doi.org/10.1109/dsaa49011.2020.00020
This work considers the problem of fitting functional models with sparsely and irregularly sampled functional data. It overcomes the limitations of the state-of-the-art methods, which face major challenges in the fitting of more complex non-linear mo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::49db1ff8c3df4a3a03faab3bd1c12ee2