Quasi-Linear Recurrent Neural Network based Identification and Predictive Control

Dazi Li, Tianjiao Kang, Jinglu Hu, Min Han, Qibing Jin

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

In this paper, aiming at the cumbersome solution of control law in neural network predictive control algorithm, a quasi-linear neural network identification and predictive control algorithm is proposed. The recurrent neural network is embedded into the quasi-linear model, which can be viewed as a quasi-ARX model macroscopically. In the quasi-linear recurrent neural network predictive control, the solution of the control law only need one-step derivation, which can greatly simplify the solution process of control law. At the same time, the quasi-linear recurrent neural network can effectively restrain the over-fitting problem in the identification process. Theoretical analysis and simulations are given to prove the simplicity and effectiveness of the proposed computing method.

本文言語English
ホスト出版物のタイトル2018 International Joint Conference on Neural Networks, IJCNN 2018 - Proceedings
出版社Institute of Electrical and Electronics Engineers Inc.
ISBN(電子版)9781509060146
DOI
出版ステータスPublished - 2018 10 10
イベント2018 International Joint Conference on Neural Networks, IJCNN 2018 - Rio de Janeiro, Brazil
継続期間: 2018 7 82018 7 13

出版物シリーズ

名前Proceedings of the International Joint Conference on Neural Networks
2018-July

Other

Other2018 International Joint Conference on Neural Networks, IJCNN 2018
国/地域Brazil
CityRio de Janeiro
Period18/7/818/7/13

ASJC Scopus subject areas

  • ソフトウェア
  • 人工知能

フィンガープリント

「Quasi-Linear Recurrent Neural Network based Identification and Predictive Control」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