NDLI: Information theoretical algorithm based on statistical models for blind identification of nonstationary dynamical systems

Content Provider	IEEE Xplore Digital Library
Author	Crippa, P. Gianfelici, F. Turchetti, C.
Copyright Year	2009
Description	Author affiliation: DIBET - Department of Biomedical Engineering, Electronics and Telecommunications, Università Politecnica delle Marche, via Brecce Bianche, I-60131 Ancona, Italy (Crippa, P.; Gianfelici, F.; Turchetti, C.)
Abstract	This paper presents an effective blind statistical identification technique for nonstationary nonlinear systems based on an information theoretical algorithm. This technique firstly extracts, from the output signals, the multivariate relationships in the Hilbert spaces by exploiting the separability properties of the signal outputs transformed by the Karhunen-Loève transform (KLT). Then, the algorithm methodologically clusters the stochastic surfaces in the Hilbert spaces using the self-organizing maps (SOMs) and further develops their best statistical model under the fixed-rank condition. The resulting blind identification of the statistical system model is based on marginal probability density functions (PDFs), whose convergence to the statistical system model based on Monte Carlo simulations has also been demonstrated by asymptotically vanishing the Kullback-Leibler divergences. A large number of simulations on both synthetic and real systems demonstrated the validity and the excellent performances of this technique that is irrespective of the system order, the stochastic surface topology, the true marginal PDFs, and the knowledge of the statistics of the noise superimposed to the output signals. Finally, this approach could also represent a suitable and promising technique for the noninvasive diagnosis of a large class of medical pathologies originated by unknown physiological factors (nonlinear compositions of unknown input signals) and/or when they are difficult or unpractical to measure.
Starting Page	612
Ending Page	618
File Size	8777045
Page Count	7
File Format	PDF
ISBN	9781424435487
ISSN	10987576
DOI	10.1109/IJCNN.2009.5178880
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-06-14
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Clustering algorithms Hilbert space Karhunen-Loeve transforms Nonlinear systems Data mining Stochastic processes Self organizing feature maps Probability density function Convergence Stochastic systems Karhunen-Loève transform Blind identification statistical identification nonlinear and nonstationary systems information theoretical learning
Content Type	Text
Resource Type	Article

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