Regular Papers

International Journal of Control, Automation, and Systems 2024; 22(3): 927-935

https://doi.org/10.1007/s12555-022-0878-x

© The International Journal of Control, Automation, and Systems

Improved Robust Passivity Criteria for Delayed Neural Networks

Yaqi Li, Yun Chen*, and Shuangcheng Sun

Northeastern University

Abstract

This paper investigates the robust passivity problem for neural networks with uncertain system parameters and a time-varying delay. Based on Lyapunov stability theory, ensuring the negative definiteness for the derivatives of the developed Lyapunov-Krasovskii functional (LKF) is necessary in order to derive a passivity criterion. A negative condition on the cubic polynomial over a certain interval is developed in this paper, which introduces some slack matrices to obtain an advanced negative condition. Taking advantage of this condition, an augmented LKF with more system state and delay function information, including several augmented vectors and a single-integralbased term, is constructed. Then some improved passivity criteria for delayed neural networks are derived on top of the proposed LKF and the negative condition. Finally, the effectiveness and superiority of the obtained passivity criteria are validated on two numerical examples.

Keywords Lyapunov-Krasovskii functional, neural networks, passivity analysis, time-varying delay

Article

Regular Papers

International Journal of Control, Automation, and Systems 2024; 22(3): 927-935

Published online March 1, 2024 https://doi.org/10.1007/s12555-022-0878-x

Copyright © The International Journal of Control, Automation, and Systems.

Improved Robust Passivity Criteria for Delayed Neural Networks

Yaqi Li, Yun Chen*, and Shuangcheng Sun

Northeastern University

Abstract

This paper investigates the robust passivity problem for neural networks with uncertain system parameters and a time-varying delay. Based on Lyapunov stability theory, ensuring the negative definiteness for the derivatives of the developed Lyapunov-Krasovskii functional (LKF) is necessary in order to derive a passivity criterion. A negative condition on the cubic polynomial over a certain interval is developed in this paper, which introduces some slack matrices to obtain an advanced negative condition. Taking advantage of this condition, an augmented LKF with more system state and delay function information, including several augmented vectors and a single-integralbased term, is constructed. Then some improved passivity criteria for delayed neural networks are derived on top of the proposed LKF and the negative condition. Finally, the effectiveness and superiority of the obtained passivity criteria are validated on two numerical examples.

Keywords: Lyapunov-Krasovskii functional, neural networks, passivity analysis, time-varying delay

IJCAS
May 2024

Vol. 22, No. 5, pp. 1461~1759

Stats or Metrics

Share this article on

  • line

Related articles in IJCAS

IJCAS

eISSN 2005-4092
pISSN 1598-6446