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 of solving 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.


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    Title :

    A novel neuro-estimator and its application to parameter estimation in a remotely piloted vehicle


    Contributors:


    Publication date :

    2000


    Size :

    6 Seiten, 17 Quellen




    Type of media :

    Article (Journal)


    Type of material :

    Print


    Language :

    English




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