Neural Network-based Tuning

Neural Network-Based Tuning is a controller tuning method that employs artificial neural networks to learn and optimize control parameters from system data, distinguishing itself from conventional tuning approaches by leveraging data-driven, nonlinear mapping capabilities for adaptive and intelligent adjustment of controller gains.

200

Publications

9.1K

Citations

517

Authors

255

Institutions

Publications per year

2017–2026

43

Authors

517

  1. SO
    Sigeru Omatu

    Osaka Prefecture University, Japan

    9 publications · 250 citations

    9

    h-index

  2. FL
    Frank L. Lewis

    The University of Texas at Arlington, United States

    8 publications · 1.2K citations

    8

    h-index

  3. CT
    Ching‐Cheng Teng

    National Yang Ming Chiao Tung University, Taiwan

    5 publications · 170 citations

    5

    h-index

  4. JJ
    Jerzy Julian Michalski

    University of Gdańsk, Poland

    4 publications · 84 citations

    4

    h-index

  5. MR
    Mircea‐Bogdan Rădac

    Polytechnic University of Timişoara, Romania

    4 publications · 171 citations

    4

    h-index

1–5 of 517

Venues

  1. IEEE Transactions on Industrial Electronics

    5 publications · 342 citations

    5

    h-index

  2. Fuzzy Sets and Systems

    5 publications · 231 citations

    5

    h-index

  3. Neurocomputing

    5 publications · 218 citations

    5

    h-index

  4. 4

    h-index

  5. Expert Systems with Applications

    4 publications · 225 citations

    4

    h-index

1–5