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Inverse optimal noise‐to‐state stabilization of stochastic recurrent neural networks driven by noise of unknown covariance
Author(s) -
Liu Ziqian,
Wang Qunjing,
Schurz Henri
Publication year - 2008
Publication title -
optimal control applications and methods
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.458
H-Index - 44
eISSN - 1099-1514
pISSN - 0143-2087
DOI - 10.1002/oca.851
Subject(s) - noise (video) , covariance , stability (learning theory) , stochastic differential equation , control theory (sociology) , artificial neural network , state (computer science) , computer science , inverse , recurrent neural network , mathematics , artificial intelligence , algorithm , machine learning , control (management) , statistics , geometry , image (mathematics)
In this paper, we extend our previous research results regarding the stabilization of recurrent neural networks from the concept of input‐to‐state stability to noise‐to‐state stability, and present a new approach to achieve noise‐to‐state stabilization in probability for stochastic recurrent neural networks driven by the noise of unknown covariance. This approach is developed by using the Lyapunov technique, inverse optimality, differential game theory, and the Hamilton–Jacobi–Isaacs equation. Numerical examples demonstrate the effectiveness of the proposed approach. Copyright © 2008 John Wiley & Sons, Ltd.