.png){kind=icon}
.png){kind=icon}
Return
Parameter identification of complex network dynamics
.png){kind=icon}
PRE
AI
DOI：10.1007/s11071-021-06482-4.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Here, we present a novel approach called the parameter identification of complex network dynamics algorithm which combines elements of the sparse identification of nonlinear dynamics algorithm with a genetic algorithm to automatically and efficiently discover the underlying dynamics of complex networks from data with minimal domain-specific knowledge requirements. Testing the proposed algorithm on empirical complex network data verifies the accuracy and efficiency of this method compared to a purely evolutionary approach.
Keywords:
Data-driven methods
Complex networks
Dynamical systems
Machine learning
Evolutionary algorithms
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.