Estimating and adjusting abnormal networks with unknown parameters and topology

Area of Science:

• Complex systems analysis
• Network science
• Control theory

Background:

• Complex networks are prone to parameter and topology changes, leading to system failures like neural diseases or circuit malfunctions.
• Adjusting abnormal networks is crucial, but often difficult due to unknown network structures and information.
• Existing methods struggle to use functional equivalents as references for abnormal network correction.

Purpose of the Study:

• To develop a novel method for estimating and controlling abnormal complex networks.
• To establish an information bridge between normal and abnormal networks for effective parameter and topology adjustment.
• To ensure the synchronization and normal functioning of corrected abnormal networks.

Main Methods:

• Design of a
• Utilization of adaptive laws to adjust abnormal network parameters and connections.
• Application of a bridging network to synchronize the abnormal network with a chosen normal reference model.
• Estimation of detailed inner network information in both normal and abnormal networks.

Main Results:

• The bridging network successfully estimates and controls abnormal network parameters and topology.
• Synchronization between the abnormal network, bridging network, and normal network is achieved.
• Accurate estimation of internal network dynamics is demonstrated.
• Nodes within the abnormal network exhibit normal behavior post-correction.

Conclusions:

• The proposed bridging network method effectively corrects abnormal complex networks.
• This approach enables accurate state estimation and control, restoring normal system function.
• The method is validated using the Hindmarsh-Rose model, showing its practical applicability.