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Nonlinear Autoregressive Model with Exogenous Input Recurrent Neural Network to Predict Satellites' Clock Bias

Yifeng Liang, Jiangning Xu, Fangneng Li, Pengfei Jiang
2021 IEEE Access  
The composition and characteristics of clock bias for satellite-borne atomic clock are described and analyzed, a clock bias prediction algorithm based on Nonlinear autoregressive model with exogenous input  ...  Satellite-borne atomic clocks are often affected by many factors in space, which makes it difficult to describe the clocks' bias and behavior with fixed model to achieve reliable high-precision prediction  ...  In reference [8] , the radial basis function (RBF) neural network is used to predict the clock bias of GPS satellite, and some better indexes are obtained compared with the traditional models.  ... 
doi:10.1109/access.2021.3053265 fatcat:y2tmcdigwnegpju5776b3q2fim

Machine learning methods for modelling and analysis of time series signals in geoinformatics [article]

Maria Kaselimi
2021 arXiv   pre-print
The first problem is related to ionospheric Total Electron Content (TEC) modeling which is an important issue in many real time Global Navigation System Satellites (GNSS) applications.  ...  GNSS users of single frequency receivers and satellite navigation systems need accurate corrections to remove signal degradation effects caused by the ionosphere.  ...  The notation ARM AX(p, q, r) refers to the model with p autoregressive terms, q moving average terms and r exogenous inputs terms.  ... 
arXiv:2109.09499v1 fatcat:6cukjkk6ivahjf56gecj7noxti

Multiple hours ahead forecast of the Dst index using a combination of Long Short-Term Memory neural network and Gaussian Process

M. A. Gruet, M. Chandorkar, A. Sicard, E. Camporeale
2018 Space Weather: The international journal of research and applications  
In this study, we present a method that combines a Long Short-Term Memory (LSTM) recurrent neural network with a Gaussian process (GP) model to provide up to 6-hr-ahead probabilistic forecasts of the Dst  ...  The proposed approach brings together the sequence modeling capabilities of a recurrent neural network with the error bars and confidence bounds provided by a GP.  ...  Authors would like to thank the CXD team at Los Alamos National Laboratory for providing GPS data.  ... 
doi:10.1029/2018sw001898 fatcat:yv52ewhgyjbmpmswxpglr2yaem

The Challenge of Machine Learning in Space Weather Nowcasting and Forecasting [article]

Enrico Camporeale
2019 arXiv   pre-print
On the other hand, this paper serves as a gentle introduction to the field of machine learning tailored to the space weather community and as a pointer to a number of open challenges that we believe the  ...  The recurring themes throughout the review are the need to shift our forecasting paradigm to a probabilistic approach focused on the reliable assessment of uncertainties, and the combination of physics-based  ...  Specifically a NARX (Nonlinear AutoRegressive with eXogenous inputs) model was presented in H.-L.  ... 
arXiv:1903.05192v2 fatcat:dteqivqsibglfkye5gkfnocici

The Challenge of Machine Learning in Space Weather: Nowcasting and Forecasting

E. Camporeale
2019 Space Weather: The international journal of research and applications  
The clearest opportunity lies in creating space weather forecasting models that can respond in real time and that are built on both physics predictions and on observed data.  ...  Space weather is a discipline that lives between academia and industry, given the relevant physical effects on satellites and power grids in a variety of applications, and the field therefore stands to  ...  We are grateful to Ryan McGranaghan for many useful discussions. No data was used.  ... 
doi:10.1029/2018sw002061 fatcat:j56q7jdpffgxde3tnxt23o64z4

Computational Socioeconomics [article]

Jian Gao, Yi-Cheng Zhang, Tao Zhou
2019 arXiv   pre-print
The understanding of socioeconomic processes provides foundations to quantify global economic development, to map regional industrial structure, and to infer individual socioeconomic status.  ...  This review, together with pioneering works we have highlighted, will draw increasing interdisciplinary attentions and induce a methodological shift in future socioeconomic studies.  ...  [91] proposed a new method based on biased Markov chain process to rank countries in a more conceptually consistent way, where a two-parameter bias is used to account for the bipartite network structure  ... 
arXiv:1905.06166v1 fatcat:kvhy2hpzgvg2vnqhdjfyjfidqi

Certifying Unstability of Switched Systems Using Sum of Squares Programming

Benoît Legat, Pablo Parrilo, Raphaël Jungers
2020 SIAM Journal of Control and Optimization  
This alternative way of analysis of slow-fast systems with particular structure may lead to a later analysis of multi-timescale systems over networks.  ...  Acknowledgements We argue that, from a modeling point of view, the assumptions of distinct timescales is ubiquitous when modeling complex systems.  ...  A NLMPC scheme with feedforward neural networks as predictive model, was useful for denitrification process [3] .  ... 
doi:10.1137/18m1173460 fatcat:ytlzbwk7vbampbuyo6snenz33m

Bioimpedance measurement based evaluation of wound healing

Atte Kekonen, Mikael Bergelin, Jan-Erik Eriksson, Annikki Vaalasti, Heimo Ylänen, Jari Viik
2017 Physiological Measurement  
In the future measurements with the method should be extended to concern hard-to-heal wounds.  ...  To our best knowledge, this is the first computational model to study the effect of the single astrocyte geometry on the Ca2+ wave propagation, while taking into account the intricate biological pathways  ...  As the complexity of computational models increases, their sample requirements grow exponentially.  ... 
doi:10.1088/1361-6579/aa63d6 pmid:28248191 fatcat:sjqhpdu7ubb2fgbgbyyu6hawxm

Behavioral and Social Science: Fifty Years of Discovery

Martin Bulmer, Neil J. Smelser, Dean R. Gerstein
1986 Contemporary Sociology  
Recent Social Trends, with its 29 separately authored chapters, nearly 1,600 pages, and foreword by President Hoover, was soon labeled and has since been informally referred to as the Ogbum report.  ...  The dominant voice proved to be that of sociologist William F. Ogbum, the director of research.  ...  inputs for exogenous variables over distant horizons.  ... 
doi:10.2307/2071061 fatcat:eaaplogpnnhwddnbditxrovcs4