Bifurcation and stabilization of a class of delayed fractional-order bidirectional associative memory inertia neural networks

Key Points

• Bifurcation behavior significantly influences the stability of neural networks with delayed feedback.
• Key finding highlights that particular configurations lead to enhanced stability in the system.
• Observational analysis across various parameter sets shows critical points of bifurcation impacting network performance.
• This work indicates a foundation for future studies on neural network stability in computational models.