Input-output Identifiability

Input-Output Identifiability is a type of model identifiability that characterizes the extent to which the internal parameters of a system can be uniquely determined from its observed input-output behavior, distinguishing it from structural or practical identifiability by focusing exclusively on the relationship between external stimuli and responses.

10

Publications

244

Citations

24

Authors

21

Institutions

Publications per year

2017–2026

1

Authors

24

  1. AM
    Alexandre Megretski

    Massachusetts Institute of Technology, United States

    1 publications · 33 citations

    1

    h-index

  2. YW
    Yuan Wang

    Florida Atlantic University, United States

    1 publications · 33 citations

    1

    h-index

  3. SB
    Samit Bhattacharyya

    Shiv Nadar University, India

    1 publications · 18 citations

    1

    h-index

  4. JJ
    Joseph J. DiStefano

    University of California, Los Angeles, United States

    1 publications · 29 citations

    1

    h-index

  5. BD
    Brian D. O. Anderson

    Australian National University, Australia

    1 publications · 12 citations

    1

    h-index

1–5 of 24

Venues

  1. IEEE Transactions on Automatic Control

    4 publications · 99 citations

    4

    h-index

  2. Mathematical Biosciences

    2 publications · 82 citations

    2

    h-index

  3. 1

    h-index

  4. IFAC Proceedings Volumes

    1 publications · 12 citations

    1

    h-index

  5. Industrial & Engineering Chemistry Research

    1 publications · 11 citations

    1

    h-index

1–5