Regular Papers

International Journal of Control, Automation, and Systems 2024; 22(2): 593-602

https://doi.org/10.1007/s12555-022-0598-2

© The International Journal of Control, Automation, and Systems

Input-to-state Practical Stability of Event-triggered Estimators for Discrete-time Recurrent Neural Networks With Unknown Time-delay

Yougang Wang, Yashuan Liu, and Sanbo Ding*

Hebei University of Technology

Abstract

In this paper, event-triggered estimators are designed for discrete-time recurrent neural networks (RNNs) with unknown time-delay. Owing to the diversity and complexity of time-delays, it is difficult to accurately predict their information. Under the boundedness of activation functions, the delay-depend term is regarded as a bounded nonlinear disturbance. Two event-triggered estimators are designed to estimate the neuron states. The first one considers the case that the system states are subject to unknown time-delay, and the second one deals with the case that both the system states and measurement outputs are subject to unknown time-delay. The sufficient conditions are developed to guarantee the input-to-state practical stability of estimation error systems. Finally, the dynamic event-triggered strategy is introduced to further reduce the events. Two numerical examples are given to show the validity of the developed scheme.

Keywords Discrete-time recurrent neural networks, event-triggered strategy, input-to-state practical stability, stability analysis, state estimation, unknown time-delay.

Article

Regular Papers

International Journal of Control, Automation, and Systems 2024; 22(2): 593-602

Published online February 1, 2024 https://doi.org/10.1007/s12555-022-0598-2

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

Input-to-state Practical Stability of Event-triggered Estimators for Discrete-time Recurrent Neural Networks With Unknown Time-delay

Yougang Wang, Yashuan Liu, and Sanbo Ding*

Hebei University of Technology

Abstract

In this paper, event-triggered estimators are designed for discrete-time recurrent neural networks (RNNs) with unknown time-delay. Owing to the diversity and complexity of time-delays, it is difficult to accurately predict their information. Under the boundedness of activation functions, the delay-depend term is regarded as a bounded nonlinear disturbance. Two event-triggered estimators are designed to estimate the neuron states. The first one considers the case that the system states are subject to unknown time-delay, and the second one deals with the case that both the system states and measurement outputs are subject to unknown time-delay. The sufficient conditions are developed to guarantee the input-to-state practical stability of estimation error systems. Finally, the dynamic event-triggered strategy is introduced to further reduce the events. Two numerical examples are given to show the validity of the developed scheme.

Keywords: Discrete-time recurrent neural networks, event-triggered strategy, input-to-state practical stability, stability analysis, state estimation, unknown time-delay.

IJCAS
February 2024

Vol. 22, No. 2, pp. 347~729

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