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Control of Recurrent Neural Networks Using Differential Minimax Game: The Stochastic 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. DSCC2010-4006, pp. 491-497; 7 pages
doi:10.1115/DSCC2010-4006
From:
  • 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

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

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