Reinforcement-driven Adaptation of Control Relations

Hans-Arno Jacobsen and Joachim Weisbrod.

In North American Conference on Fuzzy Information Processing, pages 484-488, Berkeley, USA, July 1996.


The conceptual framework of a hybrid control system architecture is briefly discussed. It employs neural and fuzzy techniques side-by-side using each one for the task to which it is best suited. Our main interest is with the adaptation of the fuzzy control knowledge. The adaptation algorithm is based on reinforcement signals and directly optimizes the global fuzzy relation representing the complete knowledge base. The new approach is experimentally evaluated.


Readers who enjoyed the above work, may also like the following:

  • Multi-client Transactions in Distributed Publish/Subscribe Systems.
    Martin Jergler, Kaiwen Zhang, and Hans-Arno Jacobsen.
    In ICDCS, 2018.
    Acceptance rate: 20%.
    Tags: publish/subscribe
  • EVA: Fair and Auditable Electric Vehicle Charging Service using Blockchain.
    Jelena Pacic, José Rivera, Kaiwen Zhang, and Hans-Arno Jacobsen.
    In DEBS, 2018.
    Tags: blockchains
  • Towards Dependable, Scalable, and Pervasive Distributed Ledgers with Blockchains.
    Kaiwen Zhang and Hans-Arno Jacobsen.
    In ICDCS, 2018.
    Tags: blockchains