Online identification of nonlinear spatiotemporal systems using kernel learning approach

Hanwen Ning, Xingjian Jing, Li Cheng

Research output: Journal article publicationJournal articleAcademic researchpeer-review

21 Citations (Scopus)


The identification of nonlinear spatiotemporal systems is of significance to engineering practice, since it can always provide useful insight into the underlying nonlinear mechanism and physical characteristics under study. In this paper, nonlinear spatiotemporal system models are transformed into a class of multi-input-multi-output (MIMO) partially linear systems (PLSs), and an effective online identification algorithm is therefore proposed by using a pruning error minimization principle and least square support vector machines. It is shown that many benchmark physical and engineering systems can be transformed into MIMO-PLSs which keep some important physical spatiotemporal relationships and are very helpful in the identification and analysis of the underlying system. Compared with several existing methods, the advantages of the proposed method are that it can make full use of some prior structural information about system physical models, can realize online estimation of the system dynamics, and achieve accurate characterization of some important nonlinear physical characteristics of the system. This would provide an important basis for state estimation, control, optimal analysis, and design of nonlinear distributed parameter systems. The proposed algorithm can also be applied to identification problems of stochastic spatiotemporal dynamical systems. Numeral examples and comparisons are given to demonstrate our results.
Original languageEnglish
Article number5959989
Pages (from-to)1381-1394
Number of pages14
JournalIEEE Transactions on Neural Networks
Issue number9
Publication statusPublished - 1 Sept 2011


  • Lattice dynamics
  • least squares support vector machines
  • nonlinear system identification
  • partially linear systems
  • spatiotemporal systems

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence


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