Robust stability of recurrent neural networks with ISS learning algorithm

被引：17
|
作者：
Ahn, Choon Ki [1 ]
机构：
[1] Seoul Natl Univ Sci & Technol, Dept Automot Engn, Seoul, South Korea
来源：
NONLINEAR DYNAMICS | 2011年 / 65卷 / 04期
关键词：
Input-to-state stability (ISS) approach; Weight learning algorithm; Dynamic neural networks; Linear matrix inequality (LMI); TO-STATE STABILITY; SMALL-GAIN THEOREM; ABSOLUTE STABILITY; SUFFICIENT CONDITION; INPUT; SYSTEMS; STABILIZATION; IDENTIFICATION;
D O I：
10.1007/s11071-010-9901-5
中图分类号：
TH [机械、仪表工业];
学科分类号：
0802 ;
摘要：
In this paper, an input-to-state stability (ISS) approach is used to derive a new robust weight learning algorithm for dynamic neural networks with external disturbance. Based on linear matrix inequality (LMI) formulation, the ISS learning algorithm is presented to not only guarantee exponential stability but also reduce the effect of an external disturbance. It is shown that the design of the ISS learning algorithm can be achieved by solving LMI, which can be easily facilitated by using some standard numerical packages. A numerical example is presented to demonstrate the validity of the proposed learning algorithm.
引用
收藏
页码：413 / 419
页数：7
相关论文
共 50 条
  • [1] Robust stability of recurrent neural networks with ISS learning algorithm
    Choon Ki Ahn
    [J]. Nonlinear Dynamics, 2011, 65 : 413 - 419
  • [2] Robust local stability of multilayer recurrent neural networks
    Suykens, JAK
    De Moor, B
    Vandewalle, J
    [J]. IEEE TRANSACTIONS ON NEURAL NETWORKS, 2000, 11 (01): : 222 - 229
  • [3] Global robust stability of delayed recurrent neural networks
    Cao, JD
    Huang, DS
    Qu, YZ
    [J]. CHAOS SOLITONS & FRACTALS, 2005, 23 (01) : 221 - 229
  • [4] A learning algorithm for improved recurrent neural networks
    Chen, CH
    Yu, LW
    [J]. 1997 IEEE INTERNATIONAL CONFERENCE ON NEURAL NETWORKS, VOLS 1-4, 1997, : 2198 - 2202
  • [5] Neural networks with robust backpropagation learning algorithm
    Walczak, B
    [J]. ANALYTICA CHIMICA ACTA, 1996, 322 (1-2) : 21 - 29
  • [6] The study of the robust learning algorithm for neural networks
    Yamawaki, Shigenobu
    [J]. KNOWLEDGE-BASED INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS, PT 2, PROCEEDINGS, 2006, 4252 : 866 - 870
  • [7] Robust stability and robust periodicity of delayed recurrent neural networks with noise disturbance
    Li, Chunguang
    Liao, Xiaofeng
    [J]. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS, 2006, 53 (10) : 2265 - 2273
  • [8] A conjugate gradient learning algorithm for recurrent neural networks
    Chang, WF
    Mak, MW
    [J]. NEUROCOMPUTING, 1999, 24 (1-3) : 173 - 189
  • [9] A FAST AND ROBUST LEARNING ALGORITHM FOR FEEDFORWARD NEURAL NETWORKS
    WEYMAERE, N
    MARTENS, JP
    [J]. NEURAL NETWORKS, 1991, 4 (03) : 361 - 369
  • [10] On the improvement of the real time recurrent learning algorithm for recurrent neural networks
    Mak, MW
    Ku, KW
    Lu, YL
    [J]. NEUROCOMPUTING, 1999, 24 (1-3) : 13 - 36
12345