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

Document Type

Conference Proceeding

Publication Date

12-1-2010

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. Copyright © 2010 by ASME.

Identifier

79958192439 (Scopus)

ISBN

[9780791844182]

Publication Title

ASME 2010 Dynamic Systems and Control Conference Dscc2010

External Full Text Location

https://doi.org/10.1115/DSCC2010-4006

First Page

491

Last Page

497

Volume

2

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