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Exponential stability analysis of neural networks with a time‐varying delay via a generalized Lyapunov‐Krasovskii functional method
Author(s) -
Li Xu,
Liu Haibo,
Liu Kuo,
Li Te,
Wang Yongqing
Publication year - 2021
Publication title -
international journal of robust and nonlinear control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.361
H-Index - 106
eISSN - 1099-1239
pISSN - 1049-8923
DOI - 10.1002/rnc.5304
Subject(s) - interval (graph theory) , stability (learning theory) , control theory (sociology) , artificial neural network , mathematics , exponential stability , exponential function , computer science , mathematical analysis , control (management) , nonlinear system , artificial intelligence , physics , machine learning , combinatorics , quantum mechanics
Summary As is known to all that the Lyapunov‐Krasovskii functional (LKF) method plays a significant role in deriving exponential stability criteria of neural networks with a time‐varying delay. However, when the LKF method is adopted, the condition that a functional is required for a neural network with a delay varying in a delay interval is so strong that it may be hard to be satisfied and lead to a conservative criterion. Therefore, a generalized LKF method is proposed by weakening the strong condition in this paper. Then, new exponential stability criteria are derived via applying the proposed method. Finally, the effectiveness of the derived criteria is verified by two numerical examples.