Learning Petri Network and its application to nonlinear system control

被引:29
|
作者
Hirasawa, K [1 ]
Ohbayashi, M
Sakai, S
Hu, JL
机构
[1] Kyushu Univ, Dept Elect & Elect Syst Engn, Fukuoka 812, Japan
[2] Kyushu Univ, Dept Energy Convers Engn, Fukuoka 812, Japan
来源
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS | 1998年 / 28卷 / 06期
关键词
back-propagation algorithm; control; neural network; Petri net; universal learning network;
D O I
10.1109/3477.735388
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页码:781 / 789
页数:9
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