Title
Weighted Null-Space Fitting For Cascade Networks With Arbitrary Location Of Sensors And Excitation Signals
Abstract
Identification of a complete dynamic network affected by sensor noise using the prediction error method is often too complex. One of the reasons for this complexity is the requirement to minimize a non-convex cost function, which becomes more difficult with more complex networks. In this paper, we consider serial cascade networks affected by sensor noise. Recently, the Weighted Null-Space Fitting method has been shown to be appropriate for this setting, providing asymptotically efficient estimates without suffering from non-convexity; however, applicability of the method was subject to some conditions on the locations of sensors and excitation signals. In this paper, we drop such conditions, proposing an extension of the method that is applicable to general serial cascade networks. We formulate an algorithm that describes application of the method in a general setting, and perform a simulation study to illustrate the performance of the method, which suggests that this extension is still asymptotically efficient.
Year
DOI
Venue
2018
10.1109/CDC.2018.8619410
2018 IEEE CONFERENCE ON DECISION AND CONTROL (CDC)
Field
DocType
ISSN
Kernel (linear algebra),Dynamic network analysis,Mean squared prediction error,Convexity,Control theory,Computer science,Algorithm,Excitation,Complex network,Cascade
Conference
0743-1546
Citations 
PageRank 
References 
0
0.34
0
Authors
3
Name
Order
Citations
PageRank
Mina Ferizbegovic101.69
Miguel Galrinho273.93
Håkan Hjalmarsson31254175.16