Special Issue: ICCAS 2024

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

Stability Analysis of Delayed Neural Networks via Modified Free-matrix Based Integral Inequality

Yongbeom Park, Ho Sub Lee, and PooGyeon Park*

POSTECH

Abstract

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.

Article

Special Issue: ICCAS 2024

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.

Stability Analysis of Delayed Neural Networks via Modified Free-matrix Based Integral Inequality

Yongbeom Park, Ho Sub Lee, and PooGyeon Park*

POSTECH

Abstract

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.

IJCAS
February 2025

Vol. 23, No. 2, pp. 359~682

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