Moving-horizon state estimation for nonlinear systems using neural networks

IEEE Transactions on Neural Networks

Volumn 22, Issue 5, 2011, Pages 768-780

  Alessandri, Angelo ^a   Baglietto, Marco ^a   Battistelli, Giorgio ^b   Gaggero, Mauro ^c  

^a UNIVERSITY OF GENOA   (Italy)

^b UNIVERSITY OF FLORENCE   (Italy)

^c INSTITUTE OF INTELLIGENT SYSTEMS FOR AUTOMATION   (Italy)

Author keywords

Moving horizon; nonlinear systems; offline optimization; state estimation

Indexed keywords

BOUNDED NOISE; COMPUTATIONAL EFFORT; ESTIMATION SCHEMES; ESTIMATION TECHNIQUES; LEAST-SQUARES COST FUNCTIONS; MEASUREMENT EQUATIONS; MOVING HORIZON; NONLINEAR DISCRETE-TIME SYSTEMS; OFF-LINE OPTIMIZATION; PARAMETERIZED; SIMULATION RESULT; SLIDING-WINDOW; SUBOPTIMAL SOLUTION;

COST FUNCTIONS; DIGITAL CONTROL SYSTEMS; DISCRETE TIME CONTROL SYSTEMS; ESTIMATION; FEEDFORWARD NEURAL NETWORKS; NONLINEAR EQUATIONS; NONLINEAR SYSTEMS; OPTIMIZATION; STATE ESTIMATION;

NONLINEAR ANALYSIS;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; ARTIFICIAL NEURAL NETWORK; COMPUTER SIMULATION; MATHEMATICAL PHENOMENA; NONLINEAR SYSTEM; PROBLEM SOLVING; STANDARD; THEORETICAL MODEL;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPUTER SIMULATION; MATHEMATICAL CONCEPTS; MODELS, THEORETICAL; NEURAL NETWORKS (COMPUTER); NONLINEAR DYNAMICS; PROBLEM SOLVING;

EID: 79955845159     PISSN: 10459227     EISSN: None     Source Type: Journal    
DOI: 10.1109/TNN.2011.2116803     Document Type: Article

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