Learning Petri network and Its application to nonlinear system control

Kotaro Hirasawa, Masanao Ohbayashi, Singo Sakai, Jinglu Hu

Research output: Contribution to journalArticle

35 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 Dec 1

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Keywords

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

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Information Systems
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering

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