Memory-aware event-triggered bipartite synchronisation of signed fractional RC–Schmitt oscillator networks
We develop a memory-aware event-triggered framework for structurally balanced networks of Schmitt-trigger relaxation oscillators whose storage branch is represented by a constant-phase element. The hybrid circuit model yields Caputo dynamics of order α∈(0,1] and, under weak signed coupling, admits a circuit-informed reduction to a signed Sakaguchi–Kuramoto network with α-dependent coupling gain and phase lag. A gauge transformation converts the balanced signed graph into its absolute graph. Exact frequency locking is established for the zero-lag model on a cohesive set, while nonzero phase lag and effective-frequency mismatch lead to explicit practical synchronisation bounds. For identical effective frequencies, the gauge disagreement decays exponentially and produces two π-separated phase groups in the original coordinates. The communication layer uses a reset transmission error together with an event-local fractional-memory term, a minimum dwell guard, and an anti-staleness timeout. A positive inter-event-time bound rules out Zeno behaviour, and a sample-and-hold perturbation analysis shows that disagreement-energy decay is preserved outside an explicit tube. Phase-level numerical experiments illustrate frequency entrainment, cohesive bipartite locking, event-count sensitivity, and node-wise triggering times.
Authors
- Suresh Rasappan (ORCID: https://orcid.org/0000-0002-6779-9000)
- Sathish Kumar Kumaravel (ORCID: https://orcid.org/0000-0002-7538-2766)
- Regan Murugesan (ORCID: https://orcid.org/0000-0002-3449-4702)
- Kala Raja Mohan (ORCID: https://orcid.org/0000-0003-2514-9111)
Institutions
- Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology (IN)
- University of Technology and Applied Sciences - Ibra (OM)
- University of Technology and Applied Sciences — Ibri (OM)
Publication Details
- Journal
- International Journal of Systems Science
- Published
- 2026-10-13
- DOI
- https://doi.org/10.1080/00207721.2026.2745917
- Primary Topic
- Neural Networks Stability and Synchronization
- Type
- article
- Field-Weighted Citation Impact
- 0.00