DocumentCode
1907056
Title
Hopfield-based adaptive state estimators
Author
Shoureshi, Rahmat ; Chu, S. Reynold
Author_Institution
Sch. of Mech. Eng., Purdue Univ., West Lafayette, IN, USA
fYear
1993
fDate
1993
Firstpage
1289
Abstract
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
Keywords
Hopfield neural nets; large-scale systems; observability; state estimation; Hopfield networks; Luenberger observers; adaptive state estimators; noise corrupted measurements; plant conditions; system identification; unknown MIMO systems; Equations; Filters; Hopfield neural networks; Intelligent networks; Linear systems; Mechanical engineering; Neurons; Observers; State estimation; System identification;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 1993., IEEE International Conference on
Conference_Location
San Francisco, CA
Print_ISBN
0-7803-0999-5
Type
conf
DOI
10.1109/ICNN.1993.298743
Filename
298743
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