Method of hybrid approximations for modelling of multidimensional nonlinear systems

dc.contributor.authorTorokhti, A.
dc.contributor.authorHowlett, P.
dc.contributor.authorPearce, C.
dc.date.issued2003
dc.descriptionThe original publication is available at www.springerlink.com
dc.description.abstractIn this paper we propose a new approach to the constructive mathematical representation of nonlinear systems transforming stochastic signals. The approach is based on a combination of a new best approximation technique and a new iterative procedure. For each iteration, the approximation is constructed as a polynomial operator of degree r which minimizes the mean–squared error between a desired output signal and the output signal of the approximating system. We show that this hybrid technique produces a computationally efficient and flexible method for modelling of nonlinear systems. The method has two degrees of freedom, the degree r of the approximating operator and the number of iterations, to decrease the associated error.
dc.description.statementofresponsibilityAnatoli Torokhti, Phil Howlett and Charles Pearce
dc.identifier.citationMultidimensional Systems and Signal Processing, 2003; 14(4):397-410
dc.identifier.doi10.1023/A:1023538920581
dc.identifier.issn0923-6082
dc.identifier.issn1573-0824
dc.identifier.urihttp://hdl.handle.net/2440/590
dc.language.isoen
dc.publisherKluwer Academic Publ
dc.rightsCopyright status unknown
dc.source.urihttp://www.springerlink.com/content/v503638530267333/
dc.subjectpseudo-inverse matrix - stochastic signals - covariance matrix - matrix computation - functional minimization
dc.titleMethod of hybrid approximations for modelling of multidimensional nonlinear systems
dc.typeJournal article
pubs.publication-statusPublished

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