Nonlinear control system using Learning Petri Network

Masanao Ohbayashi, Kotaro Hirasawa, Singo Sakai, Jinglu Hu

Research output: Contribution to journalArticlepeer-review


According to recent understanding of brain science, it is suggested that there is a distribution of functions in the brain, which means that different neurons are activated depending on which sort of sensory information the brain receives. We have already developed a learning network with a function distribution which is called the Learning Petri Network (LPN) and have shown that this network could learn nonlinear and discontinuous mappings which the Neural Network (NN) cannot. In this paper, a more realistic application which has dynamic characteristics is studied. From simulation results of a nonlinear crane control system using LPN controller, it is clarified that the control performance of LPN controller is superior to that of NN controller.

Original languageEnglish
Pages (from-to)58-69
Number of pages12
JournalElectrical Engineering in Japan (English translation of Denki Gakkai Ronbunshi)
Issue number3
Publication statusPublished - 2000 May
Externally publishedYes


  • Function distribution
  • Learning Petri network
  • Neural network
  • Nonlinear control
  • Optimization

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering


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