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Control of Recurrent Neural Networks Using Differential Minimax Game: The Deterministic Case

[+] Author Affiliations
Ziqian Liu

State University of New York Maritime College, Throggs Neck, NY

Nirwan Ansari

New Jersey Institute of Technology, Newark, NJ

Paper No. DSCC2010-4005, pp. 483-490; 8 pages
  • ASME 2010 Dynamic Systems and Control Conference
  • ASME 2010 Dynamic Systems and Control Conference, Volume 2
  • Cambridge, Massachusetts, USA, September 12–15, 2010
  • Conference Sponsors: Dynamic Systems and Control Division
  • ISBN: 978-0-7918-4418-2 | eISBN: 978-0-7918-3884-6
  • Copyright © 2010 by ASME


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



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