Real-Time Recurrent Neural State Estimation

Real-Time Recurrent Neural State Estimation

A nonlinear discrete-time neural observer for discrete-time unknown nonlinear systems in presence of external disturbances and parameter uncertainties is presented. It is based on a discrete-time recurrent high-order neural network trained with an extended Kalman-filter based algorithm. This brief i...

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Veröffentlicht in:IEEE transaction on neural networks and learning systems 2011-03, Vol.22 (3), p.497-505
Hauptverfasser: Alanis, A Y, Sanchez, E N, Loukianov, A G, Perez, M A
Format: Artikel
Sprache:eng
Schlagworte:
Algorithms
Applied sciences
Artificial Intelligence
Artificial neural networks
Computer science
control theory
systems
Computer Simulation - standards
Connectionism. Neural networks
Discrete-time nonlinear systems
Exact sciences and technology
extended Kalman filtering
Induction motors
Mathematical model
Neural Networks (Computer)
neural state estimation
Nonlinear Dynamics
Nonlinear systems
Observers
real-time implementation
recurrent neural networks
Teaching
Time Factors
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Zusammenfassung:A nonlinear discrete-time neural observer for discrete-time unknown nonlinear systems in presence of external disturbances and parameter uncertainties is presented. It is based on a discrete-time recurrent high-order neural network trained with an extended Kalman-filter based algorithm. This brief includes the stability proof based on the Lyapunov approach. The applicability of the proposed scheme is illustrated by real-time implementation for a three phase induction motor.
ISSN:1045-9227
2162-237X
1941-0093
2162-2388
DOI:10.1109/TNN.2010.2103322