Simulation study demonstrates robust synchronization in quaternion-valued neural networks with actuator faults, indicating guaranteed stability across time-delay conditions.
The synchronization of quaternion-valued bidirectional associative memory neural networks (QVBAMNNs) subject to actuator faults is studied in this work. A fault-tolerant control approach is suggested by considering both actuator effectiveness loss and bias faults, which are rarely addressed simultaneously. A mathematical fault model is formulated to characterize the influence of actuator failures on the control inputs. Under this framework, suitable guidelines are established to guarantee global exponential, fixed-time, finite-time, and predefined-time synchronization. In addition, the corresponding settling times are analyzed. Simulations are shown to confirm the theoretical results.
No takes yet. Share an insight, caveat, or question.
Jacob et al. (2026) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: