Title :
Hopfield-based adaptive state estimators
Author :
Shoureshi, Rahmat ; Chu, S. Reynold
Author_Institution :
Sch. of Mech. Eng., Purdue Univ., West Lafayette, IN, USA
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;
Conference_Titel :
Neural Networks, 1993., IEEE International Conference on
Conference_Location :
San Francisco, CA
Print_ISBN :
0-7803-0999-5
DOI :
10.1109/ICNN.1993.298743
