State-space reconstruction and spatio-temporal prediction of lattice dynamical systems

被引:66
|
作者
Guo, Lingzhong [1 ]
Billings, Stephen A. [1 ]
机构
[1] Univ Sheffield, Dept Automat Control & Syst Engn, Sheffield S1 3JD, S Yorkshire, England
基金
英国工程与自然科学研究理事会;
关键词
lattice dynamical system; orthogonal least squares algorithm; spatio-temporal prediction; state-space reconstruction;
D O I
10.1109/TAC.2007.894513
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This paper addresses the problems of state space reconstruction and spatio-temporal prediction for lattice dynamical systems. It is shown that the state space of any finite lattice dynamical system can be embedded into a reconstruction space for almost every, in the sense of prevalence, smooth measurement mapping as long as the dimension of the reconstruction space is larger than twice the size of the lattice. Based on this result, an input-output spatio-temporal dynamical relation for each site within the lattice is derived and used for spatio-temporal prediction of the system. In the case of infinite lattice dynamical systems, an approach based on constructing local lattice dynamical systems is proposed. It is shown that the finite dimensional results can be directly applied to the local modelling and spatio-temporal prediction for infinite lattice dynamical systems. Two numerical examples are provided to demonstrate the proposed theory and approach.
引用
收藏
页码:622 / 632
页数:11
相关论文
共 50 条
  • [1] A lattice control model of fuzzy dynamical systems in state-space
    Maragos, P
    Stamou, G
    Tzafestas, S
    [J]. MATHEMATICAL MORPHOLOGY AND ITS APPLICATIONS TO IMAGE AND SIGNAL PROCESSING, 2000, 18 : 61 - 70
  • [2] Lattice dynamical models of adaptive spatio-temporal phenomena
    Sinha, S
    [J]. PRAMANA-JOURNAL OF PHYSICS, 1997, 48 (01): : 287 - 302
  • [3] Lattice dynamical models of adaptive spatio-temporal phenomena
    Sudeshna Sinha
    [J]. Pramana, 1997, 48 : 287 - 302
  • [4] Learning Spatio-Temporal Specifications for Dynamical Systems
    Alsalehi, Suhail
    Aasi, Erfan
    Weiss, Ron
    Belta, Calin
    [J]. LEARNING FOR DYNAMICS AND CONTROL CONFERENCE, VOL 168, 2022, 168
  • [5] State-Space Global Coherence to Estimate the Spatio-Temporal Dynamics of the Coordinated Brain Activity
    Yousefi, Ali
    Fard, Reza Saadati
    Eden, Uri T.
    Brown, Emery N.
    [J]. 2019 41ST ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC), 2019, : 5794 - 5798
  • [6] Lattice-based spatio-temporal ensemble prediction
    Samulevicius, Saulius
    Pitarch, Yoann
    Pedersen, Torben Bach
    [J]. KNOWLEDGE-BASED AND INTELLIGENT INFORMATION & ENGINEERING SYSTEMS 18TH ANNUAL CONFERENCE, KES-2014, 2014, 35 : 494 - 503
  • [7] Predicting Latent States of Dynamical Systems With State-Space Reconstruction and Gaussian Processes
    Butler, Kurt
    Feng, Guanchao
    Mikell, Charles B.
    Mofakham, Sima
    Djuric, Petar M.
    [J]. 2022 30TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO 2022), 2022, : 2216 - 2220
  • [8] Spatio-temporal analysis with short- and long-memory dependence: a state-space approach
    Ferreira, Guillermo
    Mateu, Jorge
    Porcu, Emilio
    [J]. TEST, 2018, 27 (01) : 221 - 245
  • [9] Detection of Spatio-Temporal Recurrent Patterns in Dynamical Systems
    Bonizzi, Pietro
    Peeters, Ralf
    Zeemering, Stef
    van Hunnik, Arne
    Meste, Olivier
    Karel, Joel
    [J]. FRONTIERS IN APPLIED MATHEMATICS AND STATISTICS, 2019, 5
  • [10] Spatio-temporal analysis with short- and long-memory dependence: a state-space approach
    Guillermo Ferreira
    Jorge Mateu
    Emilio Porcu
    [J]. TEST, 2018, 27 : 221 - 245