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Nonlinear Systems Identification Using Additive Dynamic Neural Networks--Two On Line Approaches.
Griñó Cubero, Robert; Cembrano Gennari, Gabriela; Torras, Carme
Universitat Politècnica de Catalunya. Institut d'Organització i Control de Sistemes Industrials; Universitat Politècnica de Catalunya. ACES - Control Avançat de Sistemes d'Energia
This paper proposes a class of additive dynamic connectionist (ADC) models for identification of unknown dynamic systems. These models work in continuous time and are linear in their parameters. Also, for this kind of model two on-line learning or parameter adaptation algorithms are developed: one based on gradient techniques and sensitivity analysis of the model output trajectories versus the model parameters and the other based on variational calculus, that lead to an off-line solution and an invariant imbedding technique that converts the off-line solution to an on-line one. These learning methods are developed using matrix calculus techniques in order to implement them in an automatic manner with the help of a symbolic manipulation package. The good behavior of the class of identification models and the two learning methods is tested on two simulated plants and a data set from a real plant and compared, in this case, with a feedforward static (FFS) identifier.
Peer Reviewed
Àrees temàtiques de la UPC::Informàtica::Automàtica i control
Control theory
Additive dynamic neural networks
Identification
Invariant imbedding theory
Sensitivity analysis
Variational calculus
Redes neuronales
Xarxes neuronals
Identificació
Identificación
Análisis de sensibilidad
Anàlisi de sensibilitat
Cálculo de variaciones
Càlcul de variacions
Control, Teoria de
Classificació INSPEC::Control theory
Artículo
Institute of Electrical and Electronics Engineers (IEEE)
         

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