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Author Alessandri, A. ♦ Parisini, T. ♦ Zoppoli, R.
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©1998
Language English
Subject Domain (in DDC) Technology ♦ Engineering & allied operations ♦ Other branches of engineering
Subject Keyword State estimation ♦ Stochastic processes ♦ Stochastic resonance ♦ Convergence ♦ Stochastic systems ♦ Statistics ♦ Cost function ♦ Additive noise ♦ Nonlinear equations ♦ Noise measurement
Abstract A neural state estimator for nonlinear stochastic discrete-time systems is addressed. The statistics of noises are not known. The estimator is designed according to the sliding-window paradigm to minimize a quadratic estimation cost function. The noises are assumed to be additive with respect to both the state equation and the measurement channel. Sufficient conditions are devised to guarantee the convergence of the estimator and explicit asymptotic bounds on the estimation error are computed. The weights tuning technique is based on a min-max algorithm in order to guarantee the convergence of the state estimates. The estimator is designed off line in such a way as to be able to process any possible measurement pattern. This enables it to generate its estimates almost instantly.
Description Author affiliation: CNR-IAN Nat. Res. Council, Genova, Italy (Alessandri, A.)
ISBN 0780343948
ISSN 01912216
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 1998-12-18
Publisher Place USA
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Size (in Bytes) 559.80 kB
Page Count 6
Starting Page 1076
Ending Page 1081

Source: IEEE Xplore Digital Library