Quasi-Linear Recurrent Neural Network based Identification and Predictive Control

Dazi Li, Tianjiao Kang, Takayuki Furuzuki, Min Han, Qibing Jin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Title of host publication2018 International Joint Conference on Neural Networks, IJCNN 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Volume2018-July
ISBN (Electronic)9781509060146
DOIs
Publication statusPublished - 2018 Oct 10
Event2018 International Joint Conference on Neural Networks, IJCNN 2018 - Rio de Janeiro, Brazil
Duration: 2018 Jul 82018 Jul 13

Other

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

Keywords

  • control law
  • prediction control nonlinear system
  • quasi-linear
  • recurrent neural network

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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