A novel neuro-estimator and it’s application to parameter estimation in a remotely piloted vehicle

The paper presents a novel neuro-computing approach to the problem of state estimation by means of a hybrid combination of a Hopfield neural network and a feedforward multilayer neural net capable to solve certain optimization problems. This neuro-estimator is very appropriate for the real-time impl...

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Bibliographic Details
Published in:Engineering applications of artificial intelligence 2000-08, Vol.13 (4), p.459-464
Main Authors: Menhaj, Mohammad B., Rajaei Salmasi, F.
Format: Article
Language:English
Subjects:
Error covariance
Hopfield net
Neuro-estimators
Optimization
Remotely piloted vehicles
Uncertainty
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Summary:The paper presents a novel neuro-computing approach to the problem of state estimation by means of a hybrid combination of a Hopfield neural network and a feedforward multilayer neural net capable to solve certain optimization problems. This neuro-estimator is very appropriate for the real-time implementation of nonlinear state estimators, especially when the modeling of uncertainty is considered in the problem. The proposed estimator is applied to estimate the aerodynamic parameters of a remotely piloted vehicle. Simulation results show the effectiveness of the proposed method.
ISSN:0952-1976
1873-6769
DOI:10.1016/S0952-1976(00)00026-9