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State-parameter Dependency Estimation of Stochastic Time Series using Data Transformation and Parameterization by Support Vector Regression

Topics: Machine Learning in Control Applications; Modeling, Simulation and Architecture; Nonlinear Signals and Systems; System Modeling; Systems Modeling and Simulation; Systems Modeling and Simulation

Authors: Elvis Omar Jara Alegria ; Hugo Tanzarella Teixeira and Celso Pascoli Bottura

Affiliation: State University of Campinas - UNICAMP, Brazil

ISBN: 978-989-758-122-9

Keyword(s): Time Series Identification, State-dependent Parameter, Support Vector Regression.

Related Ontology Subjects/Areas/Topics: Industrial Engineering ; Informatics in Control, Automation and Robotics ; Intelligent Control Systems and Optimization ; Machine Learning in Control Applications ; Modeling, Simulation and Architectures ; Nonlinear Signals and Systems ; Robotics and Automation ; Signal Processing, Sensors, Systems Modeling and Control ; System Modeling ; Systems Modeling and Simulation

Abstract: This position paper is about the identification of the dependency among parameters and states in regression models of stochastic time series. Conventional recursive algorithms for parameter estimation do not provide good results in models with state-dependent parameters (SDP) because these may have highly non-linear behavior. To detect this dependence using conventional algorithms, we are studying some data transformations that we implement in this paper. Non-parametric relationships among parameters and states are obtained and parameterized using support vector regression. This way we look for a final non-linear structure to solve the SDP identification problem.

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Paper citation in several formats:
Omar Jara Alegria, E.; Tanzarella Teixeira, H. and Pascoli Bottura, C. (2015). State-parameter Dependency Estimation of Stochastic Time Series using Data Transformation and Parameterization by Support Vector Regression.In Proceedings of the 12th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-989-758-122-9, pages 342-347. DOI: 10.5220/0005574103420347

@conference{icinco15,
author={Elvis Omar Jara Alegria. and Hugo Tanzarella Teixeira. and Celso Pascoli Bottura.},
title={State-parameter Dependency Estimation of Stochastic Time Series using Data Transformation and Parameterization by Support Vector Regression},
booktitle={Proceedings of the 12th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2015},
pages={342-347},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005574103420347},
isbn={978-989-758-122-9},
}

TY - CONF

JO - Proceedings of the 12th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - State-parameter Dependency Estimation of Stochastic Time Series using Data Transformation and Parameterization by Support Vector Regression
SN - 978-989-758-122-9
AU - Omar Jara Alegria, E.
AU - Tanzarella Teixeira, H.
AU - Pascoli Bottura, C.
PY - 2015
SP - 342
EP - 347
DO - 10.5220/0005574103420347

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