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

Ziqian Liu, Nirwan Ansari

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

Abstract

As a continuation of our study, this paper extends our research results of optimality-oriented stabilization from deterministic recurrent neural networks to stochastic recurrent neural networks, and presents a new approach to achieve optimally stochastic input-to-state stabilization in probability for stochastic recurrent neural networks driven by noise of unknown covariance. This approach is developed by using stochastic differential minimax game, Hamilton-Jacobi- Isaacs (HJI) equation, inverse optimality, and Lyapunov technique. A numerical example is given to demonstrate the effectiveness of the proposed approach.

Original languageEnglish (US)
Title of host publicationASME 2010 Dynamic Systems and Control Conference, DSCC2010
Pages491-497
Number of pages7
DOIs
StatePublished - 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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