International Journal of Control, Automation, and Systems 2025; 23(2): 630-637
https://doi.org/10.1007/s12555-024-0523-y
© The International Journal of Control, Automation, and Systems
This paper introduces the modified free-matrix-based integral inequality (MFBII) and investigates its application in the stability analysis of delayed neural networks through the Lyapunov-Krasovskii functional (LKF) approach. In order to provide a less conservative stability criterion, the MFBII is employed with an augmented vector that contains the derivative of the system state and the nonlinear function output. A corresponding double integral of the quadratic terms related to the augmented vector is newly constructed to utilize cross-information between components in the augmented vector. Two numerical examples demonstrate the effectiveness of the proposed method.
Keywords Integral inequality, neural networks, stability analysis, time delay.
International Journal of Control, Automation, and Systems 2025; 23(2): 630-637
Published online February 1, 2025 https://doi.org/10.1007/s12555-024-0523-y
Copyright © The International Journal of Control, Automation, and Systems.
Yongbeom Park, Ho Sub Lee, and PooGyeon Park*
POSTECH
This paper introduces the modified free-matrix-based integral inequality (MFBII) and investigates its application in the stability analysis of delayed neural networks through the Lyapunov-Krasovskii functional (LKF) approach. In order to provide a less conservative stability criterion, the MFBII is employed with an augmented vector that contains the derivative of the system state and the nonlinear function output. A corresponding double integral of the quadratic terms related to the augmented vector is newly constructed to utilize cross-information between components in the augmented vector. Two numerical examples demonstrate the effectiveness of the proposed method.
Keywords: Integral inequality, neural networks, stability analysis, time delay.
Vol. 23, No. 2, pp. 359~682
Mohammad Ali Pakzad
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