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
相关论文
共 50 条
  • [1] Nonlinear control system using Learning Petri Network
    Ohbayashi, M
    Hirasawa, K
    Sakai, S
    Hu, JL
    ELECTRICAL ENGINEERING IN JAPAN, 2000, 131 (03) : 58 - 69
  • [2] Universal learning network and its application for nonlinear system with long time delay
    Han, Min
    Han, Bing
    Xi, Jianhui
    Hirasawa, Kotaro
    COMPUTERS & CHEMICAL ENGINEERING, 2006, 31 (01) : 13 - 20
  • [3] A fast learning algorithm for wavelet network and its application in control
    Zhang, Zhijun
    Chao Zhao
    2007 IEEE INTERNATIONAL CONFERENCE ON CONTROL AND AUTOMATION, VOLS 1-7, 2007, : 1991 - +
  • [4] Improvement of neural network learning algorithm and its application in control
    Wu, Y
    Shi, HB
    PROCEEDINGS OF THE 3RD WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION, VOLS 1-5, 2000, : 971 - 975
  • [5] Neural Network Augmented Intelligent Iterative Learning Control for a Nonlinear System
    Lakshmidevinivas, Devi
    Deniz, Meryem
    Balakrishnan, S. N.
    2020 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2020,
  • [6] Learning algorithm of the revised RBF network and its application to the media art system
    Kondo, Chihiro
    Kondo, Tadashi
    ARTIFICIAL LIFE AND ROBOTICS, 2010, 15 (03) : 258 - 263
  • [7] Application of Fault Model of Gun Control System Based on Petri Net
    Yuan, Bo
    Zhang, Lei
    Zha, Chen-dong
    2018 INTERNATIONAL CONFERENCE ON ELECTRICAL, CONTROL, AUTOMATION AND ROBOTICS (ECAR 2018), 2018, 307 : 30 - 35
  • [8] APPLICATION OF PETRI NETS TO SEQUENCE CONTROL
    NAGAO, Y
    URABE, H
    NAKANO, S
    KUMAGAI, S
    IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES, 1993, E76A (10) : 1598 - 1606
  • [9] OR-transition Colored Petri Net and its Application in Modeling Software System
    Yu, Yong
    Li, Tong
    Liu, Qing
    Dai, Fei
    Zhao, Na
    WKDD: 2009 SECOND INTERNATIONAL WORKSHOP ON KNOWLEDGE DISCOVERY AND DATA MINING, PROCEEDINGS, 2009, : 15 - 18
  • [10] Review of Deep Reinforcement Learning and Its Application in Modern Renewable Power System Control
    Li, Qingyan
    Lin, Tao
    Yu, Qianyi
    Du, Hui
    Li, Jun
    Fu, Xiyue
    ENERGIES, 2023, 16 (10)