Work

Robust Boundary Intermittent Stabilization of Fractional-Order Memristive Cohen–Grossberg Neural Networks

Year: 2026

Type: article

Abstract: This paper addresses the stabilization problem for a class of fractional-order memristive reaction–diffusion Cohen–Grossberg neural networks with time-varying delays under an intermittent boundary control framework. Two scenarios are considered: systems without parametric uncertainties, for which asymptotic stability is established, and systems with uncertainties, for which robust asymptotic stability is ensured. By constructing appropriate Lyapunov functionals and employing Wirtinger-type inequalities, the fractional Razumikhin approach, and key properties of the Mittag–Leffler function, sufficient stability conditions are derived in terms of linear matrix inequalities with reduced conservatism. Furthermore, the effects of time-varying delays and control activation intervals on the stabilization performance are systematically investigated. The effectiveness and advantages of the proposed control methodology are validated through numerical simulations.

Source: Fractal and Fractional

DOI: 10.3390/fractalfract10030206

Authors: Muniyappan Madhu, Saravanan Shanmugam, Srinivasan R, Mohamed Rhaima

Institutions: SRM Institute of Science and Technology, Easwari Engineering College, King Saud University

Language: English

Topic: Neural Networks Stability and Synchronization

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,258

Funder: King Saud University