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

Ivo Bukovsky, Jan Voracek, Kei Ichiji, Homma Noriyasu

2017

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.

Paper Citation

in Harvard Style

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

in Bibtex Style

@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},
}

in EndNote Style

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