Minimum driving node identification method based on complex network based on complex network strict target controllability

Abstract

The invention provides a minimum driving node identification method based on complex network strict target controllability, relates to the technical field of complex network controllability, and provides a brand-new method for identifying minimum driving nodes required by complex network strict target control. According to the method, a PBH rank criterion and a Kalman rank criterion in a control theory are utilized; the upper bound and the lower bound of the number of the driving nodes are respectively estimated when the strict target of the complex network is controllable; a method for identifying the position of the driving node in the first method is provided; on the basis, a method for quickly identifying the number and positions of upper bound driving nodes is provided. According to the invention, the method has the advantages of simple operation and wide application range, can be applied to the fields of regulation and control of a biological network, flow control of a traffic network, information propagation of a social network, safety protection of an intelligent power grid, optimal scheduling of the Internet of Vehicles and the like, can further promote the development ofthe fields of machine learning and artificial intelligence, and has important economic and social values.

Description

technical field

[0001] The invention relates to the technical field of controllability of complex networks, in particular to a method for identifying least-driven nodes based on strict target controllability of complex networks. Background technique

[0002] For a dynamical system, if there is an admissible control input that can make the state of the system reach any desired state from any initial state within a finite time, then the system is said to be controllable. In the past decade, the controllability of complex networks has received extensive attention and has become a hot issue in network science research. For complex engineering systems such as the autopilot system of aircraft and the trajectory control of satellites, it is very important to be fully controllable. But many biological, technological, and social systems are enormous in size and complexity, and controlling entire networks is neither feasible nor necessary. On the contrary, it is more realistic and n...

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