The identification of dynamic system based on memory RBF neural network

被引：0
|
作者：
Qiang, L [1 ]
Li, JX [1 ]
机构：
[1] Acad Armored Force Engn, Dept Control Engn, Beijing 100072, Peoples R China
来源：
ISTM/2005: 6th International Symposium on Test and Measurement, Vols 1-9, Conference Proceedings | 2005年
关键词：
RBF neural network; identification of dynamic system; recurrent neural network;
D O I：
暂无
中图分类号：
TH7 [仪器、仪表];
学科分类号：
0804 ; 080401 ; 081102 ;
摘要：
The memory RBF (MRBF) neural network (NN) is obtained by introducing memory neuron into the RBF neural network, and it functions a recurrent NN. The MRBF NN can identify dynamic system without feedbacking the past input and output, because the present output is related with the past input. Thus, the NN can identify the system of which the order or delay is unknown. In this paper, the learning algorithm is given and the correlative theory is proved. The simulation of dynamic system identification shows that method is valid, and can provide great potential for self-adaptive control.
引用
收藏
页码：1080 / 1083
页数：4
相关论文
共 50 条
  • [21] PD Control with RBF Neural Network Compensation for Dynamic Positioning System
    Lu, Lin-Feng
    Xu, Hai-Xiang
    Yu, Wen-Zhao
    Feng, Hui
    [J]. Chuan Bo Li Xue/Journal of Ship Mechanics, 2021, 25 (06): : 772 - 780
  • [22] Energy Demand Forecasting in China Based on Dynamic RBF Neural Network
    Zhang, Dongqing
    Ma, Kaiping
    Zhao, Yuexia
    [J]. ADVANCES IN COMPUTER SCIENCE AND EDUCATION APPLICATIONS, PT II, 2011, 202 : 388 - 395
  • [23] Color image restoration based on dynamic recurrent RBF Neural Network
    Ge, Hongwei
    Yang, Weinan
    [J]. ICNC 2007: THIRD INTERNATIONAL CONFERENCE ON NATURAL COMPUTATION, VOL 3, PROCEEDINGS, 2007, : 123 - +
  • [24] Study on Dynamic Reliability of Barrel Shell Based on RBF Neural Network
    Song Zhi-fei
    Li Hui-jun
    Li Biao
    Qin Jin-lei
    [J]. PROCEEDINGS OF THE 2017 2ND INTERNATIONAL CONFERENCE ON MATERIALS SCIENCE, MACHINERY AND ENERGY ENGINEERING (MSMEE 2017), 2017, 123 : 1265 - 1269
  • [25] Adaptive PID control strategy based on RBF neural network identification
    Zhang, MG
    Li, WH
    Liu, MQ
    [J]. PROCEEDINGS OF THE 2005 INTERNATIONAL CONFERENCE ON NEURAL NETWORKS AND BRAIN, VOLS 1-3, 2005, : 1854 - 1857
  • [26] Adaptive dynamic surface control of UAV based on RBF neural network
    Tian, Zengwu
    Zhou, Yimin
    [J]. 2021 PROCEEDINGS OF THE 40TH CHINESE CONTROL CONFERENCE (CCC), 2021, : 694 - 699
  • [27] Damage Identification of Simply Supported Bridge Based On RBF Neural Network
    Liu, Hanbing
    Jiao, Yubo
    [J]. INTERNATIONAL CONFERENCE ON GRAPHIC AND IMAGE PROCESSING (ICGIP 2012), 2013, 8768
  • [28] Heating, ventilating and airconditioning of identification based on RBF fuzzy neural network
    Yang Xiao-ping
    Li Shu-jiang
    Zhang Yan-an
    Cai Wen-jian
    [J]. Proceedings of 2005 Chinese Control and Decision Conference, Vols 1 and 2, 2005, : 716 - 719
  • [29] Research of Machine Vision System Based on RBF Neural Network
    Ge Dongyuan
    Yao Xifan
    Chen Weixiong
    Zhang Qing
    [J]. PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND INFORMATION TECHNOLOGY, 2008, : 218 - 222
  • [30] Based on RBF neural network modeling of well test system
    Tian, Jia
    [J]. Energy Engineering and Environment Engineering, 2014, 535 : 606 - 609
12345