Sensitivity of optimal control for diffusion Hopfield neural network in the presence of perturbation

被引：0
|
作者：
Wang, Quan-Fang [1 ]
Nakagiri, Shin-ichi [2 ]
机构：
[1] Chinese Univ Hong Kong, Shatin, Hong Kong, Peoples R China
[2] Kobe Univ, Fac Engn, Nada Ku, Kobe, Hyogo 6578501, Japan
来源：
APPLIED MATHEMATICS AND COMPUTATION | 2012年 / 219卷 / 08期
关键词：
Optimal control; Hopfield neural network; Perturbation; Sensitivity; Diffusion; Numerical simulation; DELAYS;
D O I：
10.1016/j.amc.2012.10.008
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
With the wide investigation of Hopfield neural network (HNN) and its control problems, this paper is to control diffusion HNN system in the presence of perturbation (disturbance, uncertainties) in the control field. In particular, it is try to answer the most interesting question on the sensitivity of these perturbations both in theoretical and computational aspects. Crown Copyright (C) 2012 Published by Elsevier Inc. All rights reserved.
引用
收藏
页码：3793 / 3808
页数：16
相关论文
共 50 条
  • [1] Optimal control with continuous Hopfield Neural Network
    Li, MA
    Ruan, XG
    [J]. 2003 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS, INTELLIGENT SYSTEMS AND SIGNAL PROCESSING, VOLS 1 AND 2, PROCEEDINGS, 2003, : 758 - 762
  • [2] Theoretical and computational issues of optimal control for distributed Hopfield neural network equations with diffusion term
    Wang, Quan-Fang
    [J]. SIAM JOURNAL ON SCIENTIFIC COMPUTING, 2007, 29 (02): : 890 - 911
  • [3] Numerical approximation of optimal control for distributed diffusion Hopfield neural networks
    Wang, Q. -F.
    [J]. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING, 2007, 69 (03) : 443 - 468
  • [4] Hopfield neural network for the optimal cost
    Minga, LM
    Feng, YQ
    Li, YJ
    [J]. PROCEEDINGS OF THE 2004 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-7, 2004, : 3277 - 3282
  • [5] Optimal control for wastewater treatment process based on Hopfield neural network
    Han, Guang
    Qiao, Jun-Fei
    Han, Hong-Gui
    Chai, Wei
    [J]. Kongzhi yu Juece/Control and Decision, 2014, 29 (11): : 2085 - 2088
  • [6] Simulation of Control Model Based on Optimal Structure of HOPFIELD Neural Network
    Liu, Jing
    [J]. MECHATRONICS ENGINEERING, COMPUTING AND INFORMATION TECHNOLOGY, 2014, 556-562 : 2370 - 2374
  • [7] Simultaneous perturbation learning rule for Hopfield neural network
    Itonaga, S
    Maeda, Y
    [J]. KNOWLEDGE-BASED INTELLIGENT INFORMATION ENGINEERING SYSTEMS & ALLIED TECHNOLOGIES, PTS 1 AND 2, 2001, 69 : 171 - 174
  • [8] Optimal Scheduling Algorithm Using Hopfield Neural Network
    Jee, Sun-Ho
    Cho, Yong-Chul
    Zhang, Liang
    Cho, Hyun-Chan
    Kang, Hee-Sun
    [J]. IMECS 2009: INTERNATIONAL MULTI-CONFERENCE OF ENGINEERS AND COMPUTER SCIENTISTS, VOLS I AND II, 2009, : 189 - +
  • [9] The study on stability of Hopfield neural network with multi-parameter perturbation
    Gao, Haibin
    Sun, Chao
    Hao, Xiaochen
    Liu, Bin
    [J]. PROCEEDINGS OF 2006 INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE: 50 YEARS' ACHIEVEMENTS, FUTURE DIRECTIONS AND SOCIAL IMPACTS, 2006, : 783 - 787
  • [10] FPGA implementation of hopfield neural network via simultaneous perturbation rule
    Wakamura, M
    Maeda, Y
    [J]. SICE 2003 ANNUAL CONFERENCE, VOLS 1-3, 2003, : 1272 - 1275
12345