Modeling dynamical systems by recurrent neural networks

被引：0
|
作者：
Zimmermann, HG
Neuneier, R
机构：
来源：
DATA MINING II | 2000年 / 2卷
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
We present our experiences of time series modeling by finite unfolding in time. The advantage of this approach is that the set of learnable neural network functions is restricted by a set of regularization methods which do not constrain the essential dynamics. Keywords in this section are over- and undershooting, the analysis of cause and effect, and the estimation of the embedding dimension in a partially externally driven dynamic system.
引用
收藏
页码：557 / 566
页数：10
相关论文
共 50 条
  • [1] Dynamical recurrent neural networks towards prediction and modeling of dynamical systems
    Aussem, A
    [J]. NEUROCOMPUTING, 1999, 28 : 207 - 232
  • [2] Modeling of continuous time dynamical systems with input by recurrent neural networks
    Chow, TWS
    Li, XD
    [J]. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-FUNDAMENTAL THEORY AND APPLICATIONS, 2000, 47 (04): : 575 - 578
  • [3] Dynamical Systems Produced by Recurrent Neural Networks
    [J]. 1600, John Wiley and Sons Inc. (31):
  • [4] Recurrent neural networks for partially observed dynamical systems
    Bhat, Uttam
    Munch, Stephan B.
    [J]. PHYSICAL REVIEW E, 2022, 105 (04)
  • [5] Identification of nonlinear dynamical systems using recurrent neural networks
    Behera, L
    Kumar, S
    Das, SC
    [J]. IEEE TENCON 2003: CONFERENCE ON CONVERGENT TECHNOLOGIES FOR THE ASIA-PACIFIC REGION, VOLS 1-4, 2003, : 1120 - 1124
  • [6] Learning dynamical systems by recurrent neural networks from orbits
    Kimura, M
    Nakano, R
    [J]. NEURAL NETWORKS, 1998, 11 (09) : 1589 - 1599
  • [7] Fuzzy knowledge and recurrent neural networks: A dynamical systems perspective
    Omlin, CW
    Giles, L
    Thornber, KK
    [J]. HYBRID NEURAL SYSTEMS, 2000, 1778 : 123 - 143
  • [8] Recurrent neural networks for dynamical systems: Applications to ordinary differential equations, collective motion, and hydrological modeling
    Gajamannage, K.
    Jayathilake, D. I.
    Park, Y.
    Bollt, E. M.
    [J]. CHAOS, 2023, 33 (01)
  • [9] Dynamical approximation by recurrent neural networks
    Garzon, M
    Botelho, F
    [J]. NEUROCOMPUTING, 1999, 29 (1-3) : 25 - 46
  • [10] Dynamical consistent recurrent neural networks
    Zimmermann, HG
    Grothmann, R
    Schäfer, AM
    Tietz, C
    [J]. PROCEEDINGS OF THE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), VOLS 1-5, 2005, : 1537 - 1541
12345