Regular Papers

International Journal of Control, Automation and Systems 2018; 16(4): 1637-1647

Published online July 25, 2018

https://doi.org/10.1007/s12555-017-0416-4

© The International Journal of Control, Automation, and Systems

Robust H∞ Performance of Discrete-time Neural Networks with Uncertainty and Time-varying Delay

M. Syed Ali, K. Meenakshi, R. Vadivel, and O. M. Kwon*

Chungbuk National University

Abstract

In this paper, we are concerned with the robust H∞ problem for a class of discrete -time neural networks with uncertainties. Under a weak assumption on the activation functional, some novel summation inequality techniques and using a new Lyapunov-Krasovskii (L-K) functional, a delay-dependent condition guaranteeing the robust asymptotically stability of the concerned neural networks is obtained in terms of a Linear Matrix Inequality(LMI). It is shown that this stability condition is less conservative than some previous ones in the literature. The controller gains can be derived by solving a set of LMIs. Finally, two numerical examples result are given to illustrate the effectiveness of the developed theoretical results."

Keywords H∞ control, linear matrix inequality, stability, time-varying delay.

Article

Regular Papers

International Journal of Control, Automation and Systems 2018; 16(4): 1637-1647

Published online August 1, 2018 https://doi.org/10.1007/s12555-017-0416-4

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

Robust H∞ Performance of Discrete-time Neural Networks with Uncertainty and Time-varying Delay

M. Syed Ali, K. Meenakshi, R. Vadivel, and O. M. Kwon*

Chungbuk National University

Abstract

In this paper, we are concerned with the robust H∞ problem for a class of discrete -time neural networks with uncertainties. Under a weak assumption on the activation functional, some novel summation inequality techniques and using a new Lyapunov-Krasovskii (L-K) functional, a delay-dependent condition guaranteeing the robust asymptotically stability of the concerned neural networks is obtained in terms of a Linear Matrix Inequality(LMI). It is shown that this stability condition is less conservative than some previous ones in the literature. The controller gains can be derived by solving a set of LMIs. Finally, two numerical examples result are given to illustrate the effectiveness of the developed theoretical results."

Keywords: H∞ control, linear matrix inequality, stability, time-varying delay.

IJCAS
January 2025

Vol. 23, No. 1, pp. 1~88

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