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Higher Order Neural Units for Efficient Adaptive Control of Weakly Nonlinear Systems

Topics: Single-neuron Modeling; Adaptive Architectures and Mechanisms; Applications: Image Processing and Artificial Vision, Pattern Recognition, Decision Making, Industrial and Real World applications, Financial Applications, Neural Prostheses and Medical Applications, Neural based Data Mining and Complex Information Processing, Neural Network Software and Applications, Applications of Deep Neural networks, Robotics and Control Applications; Stochastic Learning and Statistical Algorithms

Authors: Ivo Bukovsky 1 ; Jan Voracek 1 ; Kei Ichiji 2 and Homma Noriyasu 2

Affiliations: 1 College of Polytechnics Jihlava, Czech Republic ; 2 Tohoku University Graduate School of Medicine, Intelligent Biomedical System Engineering Laboratory and Tohoku University, Japan

ISBN: 978-989-758-274-5

Keyword(s): Polynomial Neural Networks, Higher Order Neural Units, Model Reference Adaptive Control, Conjugate Gradient, Nonlinear Dynamical Systems.

Related Ontology Subjects/Areas/Topics: Adaptive Architectures and Mechanisms ; Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Methodologies and Methods ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Theory and Methods

Abstract: The paper reviews the nonlinear polynomial neural architectures (HONUs) and their fundamental supervised batch learning algorithms for both plant identification and neuronal controller training. As a novel contribution to adaptive control with HONUs, Conjugate Gradient batch learning for weakly nonlinear plant identification with HONUs is presented as efficient learning improvement. Further, a straightforward MRAC strategy with efficient controller learning for linear and weakly nonlinear plants is proposed with static HONUs that avoids recurrent computations, and its potentials and limitations with respect to plant nonlinearity are discussed.

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Paper citation in several formats:
Bukovsky, I.; Voracek, J.; Ichiji, K. and Noriyasu, H. (2017). Higher Order Neural Units for Efficient Adaptive Control of Weakly Nonlinear Systems.In Proceedings of the 9th International Joint Conference on Computational Intelligence - Volume 1: IJCCI, ISBN 978-989-758-274-5, pages 149-157. DOI: 10.5220/0006557301490157

@conference{ijcci17,
author={Ivo Bukovsky. and Jan Voracek. and Kei Ichiji. and Homma Noriyasu.},
title={Higher Order Neural Units for Efficient Adaptive Control of Weakly Nonlinear Systems},
booktitle={Proceedings of the 9th International Joint Conference on Computational Intelligence - Volume 1: IJCCI,},
year={2017},
pages={149-157},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006557301490157},
isbn={978-989-758-274-5},
}

TY - CONF

JO - Proceedings of the 9th International Joint Conference on Computational Intelligence - Volume 1: IJCCI,
TI - Higher Order Neural Units for Efficient Adaptive Control of Weakly Nonlinear Systems
SN - 978-989-758-274-5
AU - Bukovsky, I.
AU - Voracek, J.
AU - Ichiji, K.
AU - Noriyasu, H.
PY - 2017
SP - 149
EP - 157
DO - 10.5220/0006557301490157

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