Title :
Active State Estimation for Nonlinear Systems: A Neural Approximation Approach
Author :
Scardovi, Luca ; Baglietto, Marco ; Parisini, Thomas
Author_Institution :
Univ. of Liege, Liege
fDate :
7/1/2007 12:00:00 AM
Abstract :
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.
Keywords :
Gaussian processes; entropy; infinite horizon; neurocontrollers; nonlinear programming; nonlinear systems; optimal control; probability; state estimation; stochastic systems; uncertain systems; Gaussian sum filter; Renyi entropy; active state estimation; conditional probability function; extended Ritz method; finite time horizon; information measure; neural approximation; neural control; nonlinear programming; nonlinear systems; stochastic dynamic system; stochastic optimal control; uncertainty measure minimization; Control systems; Covariance matrix; Entropy; Machine learning; Measurement uncertainty; Nonlinear systems; Optimal control; State estimation; Stochastic processes; Stochastic systems; Active estimation; entropy; neural networks (NNs); Algorithms; Computer Simulation; Decision Support Techniques; Feedback; Models, Theoretical; Neural Networks (Computer); Nonlinear Dynamics;
Journal_Title :
Neural Networks, IEEE Transactions on
DOI :
10.1109/TNN.2007.899251
