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Title:
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Nonlinear Systems Identification Using Additive Dynamic Neural Networks--Two On Line Approaches.
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Author:
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Griñó Cubero, Robert; Cembrano Gennari, Gabriela; Torras, Carme
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Other authors:
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Universitat Politècnica de Catalunya. Institut d'Organització i Control de Sistemes Industrials; Universitat Politècnica de Catalunya. ACES - Control Avançat de Sistemes d'Energia |
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Abstract:
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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. |
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Abstract:
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Peer Reviewed |
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Subject(s):
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-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 |
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Rights:
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Document type:
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Article |
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Published by:
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Institute of Electrical and Electronics Engineers (IEEE)
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