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
  • [31] A learning orientation detection system and its application to grayscale images
    Chen, Tianqi
    Todo, Yuki
    Zhang, Zeyu
    Qiu, Zhiyu
    Hua, Yuxiao
    Tang, Zheng
    KNOWLEDGE-BASED SYSTEMS, 2025, 310
  • [32] Application of Nonlinear Estimation Strategies on a Magnetorheological Suspension System with Skyhook Control
    Lee, Andrew S.
    Gadsden, S. Andrew
    Al-Shabi, Mohammad
    2020 IEEE INTERNATIONAL IOT, ELECTRONICS AND MECHATRONICS CONFERENCE (IEMTRONICS 2020), 2020, : 158 - 163
  • [33] The Design and Application of Control System Based on the BP Neural Network
    Li, Xinglei
    Yu, Hongbin
    PROCEEDINGS OF THE 3RD INTERNATIONAL CONFERENCE ON MECHANICAL ENGINEERING AND INTELLIGENT SYSTEMS (ICMEIS 2015), 2015, 26 : 789 - 793
  • [34] An Intelligent Control System Construction Using High-level Time Petri Net And Reinforcement Learning
    Feng, Liangbing
    Obayashi, Masanao
    Kuremoto, Takashi
    Kobayashi, Kunikazu
    INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION AND SYSTEMS (ICCAS 2010), 2010, : 535 - 539
  • [35] Neural Network-Based Optimal Tracking Control of Continuous-Time Uncertain Nonlinear System via Reinforcement Learning
    Jingang Zhao
    Neural Processing Letters, 2020, 51 : 2513 - 2530
  • [36] Neural Network-Based Optimal Tracking Control of Continuous-Time Uncertain Nonlinear System via Reinforcement Learning
    Zhao, Jingang
    NEURAL PROCESSING LETTERS, 2020, 51 (03) : 2513 - 2530
  • [37] Self-learning fuzzy neural network and its application to fire auto-detecting in fire protection system
    Chen, SY
    Yi, JK
    Zhao, YY
    PROCEEDINGS OF THE 3RD WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION, VOLS 1-5, 2000, : 1754 - 1757
  • [38] Protocol syntheses in a Petri net model with registers and its application
    Yamaguchi, H
    Okano, K
    Higashino, T
    Taniguchi, K
    ELECTRONICS AND COMMUNICATIONS IN JAPAN PART III-FUNDAMENTAL ELECTRONIC SCIENCE, 1998, 81 (08): : 18 - 26
  • [39] The Research on Workflow Model Based on Petri Net and Its Application
    Gu, Weijie
    Qian, Yuexia
    Wang, Jishui
    2011 INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING AND MULTIMEDIA COMMUNICATION, 2011, : 442 - 445
  • [40] On the equivalence of FSM and Petri net and its application in EC negotiation
    Ji, SJ
    Liang, YQ
    Wu, ZH
    Tian, QJ
    PROCEEDINGS OF THE 2004 INTERNATIONAL CONFERENCE ON MANAGEMENT SCIENCE & ENGINEERING, VOLS 1 AND 2, 2004, : 84 - 88