Non-linear system control using learning Petri network

Author

Ohbayashi, Masanao ; Hirasawa, Kotaro ; Sakai, Singo ; Yu, Yunqing

Author_Institution

Dept. of Electr. & Electron. Syst. Eng., Kyushu Univ., Fukuoka, Japan

Volume

2

fYear

1997

fDate

9-12 Jun 1997

Firstpage

854

Abstract

According to the recent knowledge of brain science, it is suggested that there exists function distribution in the brain, which means that different neurons are activated depending on which sort of sensory information the brain receives. We have previously (1995, 1996) developed a learning network with function distribution which is called learning Petri network (LPN) and have also shown that this network could learn nonlinear and discontinuous mappings which neural network (NN) can not learn. 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 has been proved that the control performance of LPN controller is superior to that of NN controller

Keywords

Petri nets; cranes; learning (artificial intelligence); materials handling; nonlinear control systems; brain; crane control system; discontinuous mappings; function distribution; learning Petri network; nonlinear mappings; nonlinear system control; Biological neural networks; Control system synthesis; Control systems; Cranes; Delay effects; Neural networks; Nonlinear control systems; Nonlinear dynamical systems; Nonlinear systems; Pattern recognition;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks,1997., International Conference on

Conference_Location

Houston, TX

Print_ISBN

0-7803-4122-8

Type

conf

DOI

10.1109/ICNN.1997.616136

Filename

616136