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A multiresolution wavelet based subspace identification

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This paper presents multiresolution subspace identification as an extension of classical subspace modeling, thus inheriting features of robust subspace identification with added advantages of wavelet based modeling enabling multiresolution state-space model development. Identification of a noisy process in presence of mild nonlinearity can be approximated by estimating multiple multiresolution time invariant models. Parameter estimation in projection space at appropriate scales is achieved using least squares method. The efficacy of the proposed approach has been demonstrated by modeling nuclear reactor in prediction as well as simulation environment. It is shown that root mean squared error reduces significantly as compared to their single scale counterparts providing better modeling performances.

Original languageEnglish
Pages (from-to)247-253
Number of pages7
Issue number1
Publication statusPublished - 19 Apr 2016
Externally publishedYes
Event4th International Conference on Advances in Control and Optimization of Dynamical Systems - NIT Tiruchirappalli, Tiruchirappalli, India
Duration: 1 Feb 20165 Feb 2016

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