DocumentCode :
3601261
Title :
Spatiotemporal System Identification With Continuous Spatial Maps and Sparse Estimation
Author :
Aram, Parham ; Kadirkamanathan, Visakan ; Anderson, Sean R.
Author_Institution :
Dept. of Autom. Control & Syst. Eng., Univ. of Sheffield, Sheffield, UK
Volume :
26
Issue :
11
fYear :
2015
Firstpage :
2978
Lastpage :
2983
Abstract :
We present a framework for the identification of spatiotemporal linear dynamical systems. We use a state-space model representation that has the following attributes: 1) the number of spatial observation locations are decoupled from the model order; 2) the model allows for spatial heterogeneity; 3) the model representation is continuous over space; and 4) the model parameters can be identified in a simple and sparse estimation procedure. The model identification procedure we propose has four steps: 1) decomposition of the continuous spatial field using a finite set of basis functions where spatial frequency analysis is used to determine basis function width and spacing, such that the main spatial frequency contents of the underlying field can be captured; 2) initialization of states in closed form; 3) initialization of state-transition and input matrix model parameters using sparse regression-the least absolute shrinkage and selection operator method; and 4) joint state and parameter estimation using an iterative Kalman-filter/sparse-regression algorithm. To investigate the performance of the proposed algorithm we use data generated by the Kuramoto model of spatiotemporal cortical dynamics. The identification algorithm performs successfully, predicting the spatiotemporal field with high accuracy, whilst the sparse regression leads to a compact model.
Keywords :
Kalman filters; iterative methods; linear systems; matrix algebra; parameter estimation; regression analysis; state-space methods; Kuramoto model; basis function width; continuous spatial field decomposition; continuous spatial maps; input matrix model parameters; iterative Kalman-filter-sparse-regression algorithm; least absolute shrinkage; model identification procedure; model parameter identification; parameter estimation; selection operator method; sparse estimation; sparse regression; spatial frequency analysis; spatial heterogeneity; spatial observation locations; spatiotemporal cortical dynamics; spatiotemporal linear dynamical systems; spatiotemporal system identification algorithm; state-space model representation; state-transition initialization; Autoregressive processes; Estimation; Joints; Mathematical model; Modeling; Spatiotemporal phenomena; State-space methods; Space-time modeling; sparse regression; spatiotemporal; system identification; system identification.;
fLanguage :
English
Journal_Title :
Neural Networks and Learning Systems, IEEE Transactions on
Publisher :
ieee
ISSN :
2162-237X
Type :
jour
DOI :
10.1109/TNNLS.2015.2392563
Filename :
7027840
Link To Document :
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