Skip to main navigation Skip to search Skip to main content

Interpretable PID parameter tuning for control engineering using general dynamic neural networks: An extensive comparison

  • Johannes Günther
  • , Elias Reichensdörfer
  • , Patrick M. Pilarski
  • , Klaus Diepold
  • University of Alberta
  • Alberta Innovates Centre for Machine Learning
  • Technical University of Munich

Research output: Contribution to journal › Article › peer-review

25 Scopus citations

Fingerprint

Dive into the research topics of 'Interpretable PID parameter tuning for control engineering using general dynamic neural networks: An extensive comparison'. Together they form a unique fingerprint.
Sort by

Keyphrases

Computer Science