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An efficient recursive identification algorithm for multilinear systems based on tensor decomposition
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DOI：10.1002/rnc.5718.png)
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Abstract
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There are many important fields involving the multilinear system identification. A great number of parameters to be identified is an important challenge, leading to the need for tensorial decomposition and modeling of such systems. This article is about the parameter estimation of the higher-order multilinear systems with non-Gaussian noises and to explore the role of tensor algebra in the multilinear model identification. A high-dimension system identification problem is reformulated in terms of low-dimension problems by using the tensorial decomposition technique. Further, applying the multi-innovation identification theory, the recursive algorithm combining with the logarithmic p-norms is investigated for multilinear systems with non-Gaussian noises of low computational complexity. Finally, some simulation results illustrate the effectiveness of the proposed recursive identification method.
Keywords:
multi-innovation identification theory
multilinear systems
parameter estimation
recursive identification
tensor decomposition
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