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

Document Type

Conference Proceeding

Publication Date

12-1-2010

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. Copyright © 2010 by ASME.

Identifier

79958202188 (Scopus)

ISBN

[9780791844182]

Publication Title

ASME 2010 Dynamic Systems and Control Conference Dscc2010

External Full Text Location

https://doi.org/10.1115/DSCC2010-4005

First Page

483

Last Page

490

Volume

2

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