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Nonlinear control system using learning Petri network

✍ Scribed by Masanao Ohbayashi; Kotaro Hirasawa; Singo Sakai; Jinglu Hu

Publisher: John Wiley and Sons
Year: 2000
Tongue: English
Weight: 285 KB
Volume: 131
Category: Article
ISSN: 0424-7760
DOI: 10.1002/(sici)1520-6416(200003)131:3<58::aid-eej7>3.0.co;2-f

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✦ Synopsis

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.

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