Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/24585
Title: Online identification of nonlinear spatiotemporal systems using kernel learning approach
Authors: Ning, H
Jing, X 
Cheng, L 
Keywords: Lattice dynamics
Least squares support vector machines
Nonlinear system identification
Partially linear systems
Spatiotemporal systems
Issue Date: 2011
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on neural networks, 2011, v. 22, no. 9, 5959989, p. 1381-1394 How to cite?
Journal: IEEE transactions on neural networks 
Abstract: 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.
URI: http://hdl.handle.net/10397/24585
ISSN: 1045-9227
DOI: 10.1109/TNN.2011.2161331
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