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Active State Estimation for Nonlinear Systems: A Neural Approximation Approach

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3 Author(s)
Scardovi, L. ; Univ. of Liege, Liege ; Baglietto, M. ; Parisini, T.

In this paper, we consider the problem of actively providing an estimate of the state of a stochastic dynamic system over a (possibly long) finite time horizon. The active estimation problem (AEP) is formulated as a stochastic optimal control one, in which the minimization of a suitable uncertainty measure is carried out. Toward this end, the use of the Renyi entropy as an information measure is proposed and motivated. A neural control scheme, based on the application of the extended Ritz method (ERIM) and on the use of a Gaussian sum filter (GSF), is then presented. Simulation results show the effectiveness of the proposed approach.

Published in:

Neural Networks, IEEE Transactions on  (Volume:18 ,  Issue: 4 )

Date of Publication:

July 2007

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