Abstract:
Graphical models are widely used to model the statistical relationships among variables in a system. Existing graphical models can be used to model the relationships amon...Show MoreMetadata
Abstract:
Graphical models are widely used to model the statistical relationships among variables in a system. Existing graphical models can be used to model the relationships among scalar variables, but cannot be directly applied to model a system with functional variables. In this paper, a novel functional graphical model is proposed to model complex systems where functional variables are measured. To cope with the small sample size problem, we further develop a special sparsity penalization approach to robustly learn the graphical model from limited sample size, and develop a difference from the mean penalization for functional variables. Simulation studies and a case study in a plasma spray manufacturing process are used to demonstrate the effectiveness of the proposed method.
Published in: IEEE Transactions on Automation Science and Engineering ( Volume: 14, Issue: 4, October 2017)