Input Identifiability is a property of a mathematical model that specifies the extent to which the model's input variables can be uniquely determined from observed output data under given structural and parametric constraints.
5
Publications
628
Citations
13
Authors
12
Institutions
Publications per year
2017–2026
1
Parents
Publications
5
1–5 of 5
Authors
13
- SB
1
h-index
- BS
1
h-index
- GC
1
h-index
- TY
1
h-index
- HR
1
h-index
1–5 of 13
Institutions
12
Marshall Space Flight Center
Redstone Arsenal, United States
1 h-index
2 publications
36 citations
Systems Dynamics (United States)
Huntsville, United States
1 h-index
2 publications
36 citations
Universidade Federal do Rio de Janeiro
Rio de Janeiro, Brazil
1 h-index
1 publications
18 citations
Massachusetts Institute of Technology
Cambridge, United States
1 h-index
1 publications
33 citations
Rutgers, The State University of New Jersey
New Brunswick, United States
1 h-index
1 publications
33 citations
Harvard University
Cambridge, United States
1 h-index
1 publications
535 citations
Kyoto University
Kyoto, Japan
1 h-index
1 publications
18 citations
Hohai University
Nanjing, China
1 h-index
2 publications
58 citations
1–8 of 12
Venues
- IEEE Transactions on Automatic Control
2 publications · 51 citations
2
h-index
- Computers & Chemical Engineering
1 publications · 13 citations
1
h-index
- IEE Proceedings - Generation Transmission and Distribution
1 publications · 29 citations
1
h-index
- Journal of Hydrology
1 publications · 535 citations
1
h-index
1–4