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Nonlinear Adaptive Estimation by Kernel Least Mean Square with Surprise Criterion and Parallel Hyperslab Projection along Affine Subspaces Algorithm

Topics: Adaptive Signal Processing and Control; Engineering Applications on Intelligent Control Systems and Optimization; Machine Learning in Control Applications; Nonlinear Signals and Systems; System Modeling

Authors: Angie Forero and Celso P. Bottura

Affiliation: School of Electrical and Computer Engineering, University of Campinas and Brazil

ISBN: 978-989-758-321-6

Keyword(s): Adaptive Nonlinear Estimation, Machine Learning, Kernel Algorithms, Kernel Least Mean Square, Surprise Criterion, Projection along Affine Subspaces.

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

Abstract: In this paper the algorithm KSCP (KLMS with Surprise Criterion and Parallel Hyperslab Projection Along Affine Subspaces) for adaptive estimation of nonlinear systems is proposed. It is based on the combination of: - the reproducing kernel to deal with the high complexity of nonlinear systems; -the parallel hyperslab projection along affine subspace learning algorithm, to deal with adaptive nonlinear estimation problem; - the kernel least mean square with surprise criterion that uses concepts of likelihood and bayesian inference to predict the posterior distribution of data, guaranteeing an appropriate selection of data to the dictionary at low computational cost, to deal with the exponential growth of the dictionary, as new data arrives. The proposed algorithm offers high accuracy estimation and high velocity of computation, characteristics that are very important in estimation and tracking online applications.

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Paper citation in several formats:
Forero A. and Bottura C. (2018). Nonlinear Adaptive Estimation by Kernel Least Mean Square with Surprise Criterion and Parallel Hyperslab Projection along Affine Subspaces Algorithm.In Proceedings of the 15th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-989-758-321-6, pages 362-368. DOI: 10.5220/0006867803720378

@conference{icinco18,
author={Angie Forero and Celso P. Bottura},
title={Nonlinear Adaptive Estimation by Kernel Least Mean Square with Surprise Criterion and Parallel Hyperslab Projection along Affine Subspaces Algorithm},
booktitle={Proceedings of the 15th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2018},
pages={362-368},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006867803720378},
isbn={978-989-758-321-6},
}

TY - CONF

JO - Proceedings of the 15th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - Nonlinear Adaptive Estimation by Kernel Least Mean Square with Surprise Criterion and Parallel Hyperslab Projection along Affine Subspaces Algorithm
SN - 978-989-758-321-6
AU - Forero A.
AU - Bottura C.
PY - 2018
SP - 362
EP - 368
DO - 10.5220/0006867803720378

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