Higher Order Neural Units for Efficient Adaptive Control of Weakly Nonlinear Systems
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F71226401%3A_____%2F18%3AN0100103" target="_blank" >RIV/71226401:_____/18:N0100103 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.5220/0006557301490157" target="_blank" >http://dx.doi.org/10.5220/0006557301490157</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.5220/0006557301490157" target="_blank" >10.5220/0006557301490157</a>
Alternative languages
Result language
angličtina
Original language name
Higher Order Neural Units for Efficient Adaptive Control of Weakly Nonlinear Systems
Original language description
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.
Czech name
—
Czech description
—
Classification
Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
20204 - Robotics and automatic control
Result continuities
Project
—
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2018
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the 9th International Joint Conference on Computational Intelligence
ISBN
978-989-758-274-5
ISSN
—
e-ISSN
—
Number of pages
9
Pages from-to
149-157
Publisher name
SCITEPRESS – Science and Technology Publications, Lda.
Place of publication
Portugal
Event location
Funchal, Madeira, Portugal
Event date
Nov 1, 2017
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
—