Finite-time Stability of Delayed Recurrent Neural Networks with the Effect of Impulsive Control

J. Jayabharathi; Khang Wen Goh; Lakshmanan Shanmugam

doi:10.1007/s11063-025-11830-1

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Finite-time Stability of Delayed Recurrent Neural Networks with the Effect of Impulsive Control

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J. Jayabharathi, Khang Wen Goh, Lakshmanan Shanmugam

2026 Neural Processing Letters Vol. 58 Issue 2 Article Cited by 0 SDG 17SDG 9SDG 16

Abstract

This article deals with the finite-time stability (FTS) problem of delayed recurrent neural networks (DRNNs) under the effect of impulsive control. To do this analysis, a suitable Lyapunov-Krasovskii functional is constructed with information about the delay bounds and an impulsive instant, and the corresponding sufficient conditions are derived in the form of linear matrix inequalities. Besides that, the activation functions do not necessitate boundedness or monotonicity. The sufficient conditions ensure the FTS of the DRNNs under impulsive control. Finally, the derived criteria are validated via a numerical example and their simulation results, which confirm the efficiency of the proposed results. © The Author(s) 2026.

Affiliations

Department of Mathematics, Vellore Institute of Technology, Tamil Nadu, Chennai, 600127, India; Faculty of Business and Communication, INTI International University, Nilai, Malaysia; Faculty of Mathematics and Natural Sciences, Universitas Negeri Padang, Padang, Indonesia

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