Control of recurrent neural networks using differential minimax game: The deterministic case

Ziqian Liu, Nirwan Ansari

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

1 Scopus citations

Abstract

This paper presents a theoretical design of how a minimax equilibrium of differential game is achieved in a class of large-scale nonlinear dynamic systems, namely the recurrent neural networks. In order to realize the equilibrium, we consider the vector of external inputs as a player and the vector of internal noises (or disturbances or modeling errors) as an opposing player. The purpose of this study is to construct a nonlinear H optimal control for deterministic noisy recurrent neural networks to achieve an optimal-oriented stabilization, as well as to attenuate noise to a prescribed level with stability margins. A numerical example demonstrates the effectiveness of the proposed approach.

Original languageEnglish (US)
Title of host publicationASME 2010 Dynamic Systems and Control Conference, DSCC2010
Pages483-490
Number of pages8
DOIs
StatePublished - Dec 1 2010
EventASME 2010 Dynamic Systems and Control Conference, DSCC2010 - Cambridge, MA, United States
Duration: Sep 12 2010Sep 15 2010

Publication series

NameASME 2010 Dynamic Systems and Control Conference, DSCC2010
Volume2

Other

OtherASME 2010 Dynamic Systems and Control Conference, DSCC2010
CountryUnited States
CityCambridge, MA
Period9/12/109/15/10

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering

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