Higher order neural units for efficient adaptive control of weakly nonlinear systems

Ivo Bukovsky, Jan Voracek, Kei Ichiji, Homma Noriyasu

研究成果: Conference contribution

2 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトルIJCCI 2017 - Proceedings of the 9th International Joint Conference on Computational Intelligence
編集者Christophe Sabourin, Juan Julian Merelo, Una-May O'Reilly, Kurosh Madani, Kevin Warwick
出版社SciTePress
ページ149-157
ページ数9
ISBN(印刷版)9789897582745
DOI
出版ステータスPublished - 2017
イベント9th International Joint Conference on Computational Intelligence, IJCCI 2017 - Funchal, Madeira, Portugal
継続期間: 2017 11 12017 11 3

出版物シリーズ

名前IJCCI 2017 - Proceedings of the 9th International Joint Conference on Computational Intelligence

Other

Other9th International Joint Conference on Computational Intelligence, IJCCI 2017
CountryPortugal
CityFunchal, Madeira
Period17/11/117/11/3

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Computer Graphics and Computer-Aided Design
  • Software

フィンガープリント 「Higher order neural units for efficient adaptive control of weakly nonlinear systems」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル