Learning Petri network and Its application to nonlinear system control

Kotaro Hirasawa, Masanao Ohbayashi, Singo Sakai, Takayuki Furuzuki

Research output: Contribution to journalArticle

34 Citations (Scopus)

Abstract

According to recent knowledge of brain science, it is suggested that there exists functions distribution, which means that specific parts exist in the brain for realizing specific functions. This paper introduces a new brain-like model called Learning Petri Network (LPN) that has the capability of functions distribution and learning. The idea is to use Petri net to realize the functions distribution and to incorporate the learning and representing ability of neural network into the Petri net. The obtained LPN can be used in the same way as a neural network to model and control dynamic systems, while it is distinctive to a neural network in that it has the capability of functions distribution. An application of the LPN to nonlinear crane control systems is discussed. It is shown via numerical simulations that the proposed LPN controller has superior performance to the commonly-used neural network one.

Original languageEnglish
Pages (from-to)781-789
Number of pages9
JournalIEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Volume28
Issue number6
DOIs
Publication statusPublished - 1998
Externally publishedYes

Fingerprint

Nonlinear control systems
Distribution functions
Neural networks
Brain
Petri nets
Cranes
Dynamical systems
Control systems
Controllers
Computer simulation

Keywords

  • Back-propagation algorithm
  • Control
  • Neural network
  • Petri net
  • Universal learning network

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Artificial Intelligence
  • Human-Computer Interaction

Cite this

Learning Petri network and Its application to nonlinear system control. / Hirasawa, Kotaro; Ohbayashi, Masanao; Sakai, Singo; Furuzuki, Takayuki.

In: IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, Vol. 28, No. 6, 1998, p. 781-789.

Research output: Contribution to journalArticle

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