Hopfield-based adaptive state estimators

Hopfield-based adaptive state estimators

Hopfield networks have been applied to the problem of system identification. Luenberger observers have long been used for estimation of unmeasurable states of linear systems. The mathematical derivation of an adaptive observer based on integration of the two techniques is presented. The identificati...

Ausführliche Beschreibung

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Bibliographische Detailangaben
Hauptverfasser: Shoureshi, R., Chu, S.R.
Format: Tagungsbericht
Sprache:eng
Schlagworte:
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Hopfield neural networks
Intelligent networks
Linear systems
Mechanical engineering
Neurons
Observers
State estimation
System identification
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Beschreibung
Zusammenfassung:Hopfield networks have been applied to the problem of system identification. Luenberger observers have long been used for estimation of unmeasurable states of linear systems. The mathematical derivation of an adaptive observer based on integration of the two techniques is presented. The identification of unknown multiple input multiple output (MIMO) systems with noise corrupted measurements is described. Simulation results for different plant conditions are detailed.< >
DOI:10.1109/ICNN.1993.298743